init
This commit is contained in:
+50
@@ -0,0 +1,50 @@
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||||
PROJECT(openvibe-plugins-signal-processing)
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||||
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SET(PROJECT_VERSION_MAJOR ${OV_GLOBAL_VERSION_MAJOR})
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SET(PROJECT_VERSION ${OV_GLOBAL_VERSION_STRING})
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||||
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||||
FILE(GLOB_RECURSE SRC_FILES src/*.cpp src/*.h src/*.hpp src/*.inl)
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ADD_LIBRARY(${PROJECT_NAME} SHARED ${SRC_FILES}
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"../../../contrib/packages/wavelet2d/wavelet2s.cpp"
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"../../../contrib/packages/wavelet2d/wavelet2s.h")
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target_include_directories(${PROJECT_NAME}
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PRIVATE
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src
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src/algorithms/connectivity
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src/algorithms/basic)
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SET_TARGET_PROPERTIES(${PROJECT_NAME} PROPERTIES
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VERSION ${PROJECT_VERSION}
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SOVERSION ${PROJECT_VERSION_MAJOR}
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FOLDER ${PLUGINS_FOLDER}
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COMPILE_FLAGS "-DOVP_Exports -DOVP_Shared")
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INCLUDE_DIRECTORIES("../../../contrib/packages/wavelet2d")
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ADD_DEFINITIONS(-D_USE_MATH_DEFINES)
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# ---------------------------------
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INCLUDE("FindOpenViBE")
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INCLUDE("FindOpenViBECommon")
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INCLUDE("FindOpenViBEToolkit")
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INCLUDE("FindOpenViBEModuleEBML")
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INCLUDE("FindThirdPartyBoost")
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INCLUDE("FindThirdPartyEigen")
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INCLUDE("FindThirdPartyFFTW3") # used by the wavelet library
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INCLUDE("FindThirdPartyITPP") # note that itpp gives the fftw3 on Win
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IF(OV_COMPILE_TESTS)
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ADD_SUBDIRECTORY("test")
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ENDIF(OV_COMPILE_TESTS)
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# -----------------------------
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# Install files
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# -----------------------------
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INSTALL(TARGETS ${PROJECT_NAME}
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RUNTIME DESTINATION ${DIST_BINDIR}
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LIBRARY DESTINATION ${DIST_LIBDIR}
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ARCHIVE DESTINATION ${DIST_LIBDIR})
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INSTALL(DIRECTORY signals/ DESTINATION ${DIST_DATADIR}/openvibe/scenarios/signals)
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INSTALL(DIRECTORY box-tutorials/ DESTINATION ${DIST_DATADIR}/openvibe/scenarios/box-tutorials)
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+600
@@ -0,0 +1,600 @@
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||||
<OpenViBE-Scenario>
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||||
<FormatVersion>1</FormatVersion>
|
||||
<Creator>openvibe</Creator>
|
||||
<CreatorVersion>2.0</CreatorVersion>
|
||||
<Boxes>
|
||||
<Box>
|
||||
<Identifier>(0x00000503, 0x00001d65)</Identifier>
|
||||
<Name>1;4</Name>
|
||||
<AlgorithmClassIdentifier>(0x361722e8, 0x311574e8)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Input signal</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Output signal</Name>
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||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x79a9edeb, 0x245d83fc)</TypeIdentifier>
|
||||
<Name>Channel List</Name>
|
||||
<DefaultValue>:</DefaultValue>
|
||||
<Value>1;4</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x3bcf9e67, 0x0c23994d)</TypeIdentifier>
|
||||
<Name>Action</Name>
|
||||
<DefaultValue>Select</DefaultValue>
|
||||
<Value>Select</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x666f25e9, 0x3e5738d6)</TypeIdentifier>
|
||||
<Name>Channel Matching Method</Name>
|
||||
<DefaultValue>Smart</DefaultValue>
|
||||
<Value>Smart</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>160.000000</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa963f5, 0x1a638cd4)</Identifier>
|
||||
<Value>51</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>336.000000</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x30a4e5c9, 0x83502953)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x277826e1, 0xa30a3bd0)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x527ad68d, 0x16d746a0)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xad100179, 0xa3c984ab)</Identifier>
|
||||
<Value>106</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>3</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x00000ba1, 0x00004ae1)</Identifier>
|
||||
<Name>Before</Name>
|
||||
<AlgorithmClassIdentifier>(0x0055be5f, 0x087bdd12)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Data</Name>
|
||||
</Input>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
|
||||
<Name>Stimulations</Name>
|
||||
</Input>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x6ab26b81, 0x0f8c02f3)</TypeIdentifier>
|
||||
<Name>Channel Units</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x5de046a6, 0x086340aa)</TypeIdentifier>
|
||||
<Name>Display Mode</Name>
|
||||
<DefaultValue>Scan</DefaultValue>
|
||||
<Value>Scan</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x33a30739, 0x00d5299b)</TypeIdentifier>
|
||||
<Name>Auto vertical scale</Name>
|
||||
<DefaultValue>Per channel</DefaultValue>
|
||||
<Value>Per channel</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Scale refresh interval (secs)</Name>
|
||||
<DefaultValue>5</DefaultValue>
|
||||
<Value>5</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Vertical Scale</Name>
|
||||
<DefaultValue>100</DefaultValue>
|
||||
<Value>100</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Vertical Offset</Name>
|
||||
<DefaultValue>0</DefaultValue>
|
||||
<Value>0</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Time Scale</Name>
|
||||
<DefaultValue>10</DefaultValue>
|
||||
<Value>10</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Horizontal ruler</Name>
|
||||
<DefaultValue>true</DefaultValue>
|
||||
<Value>true</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Vertical ruler</Name>
|
||||
<DefaultValue>false</DefaultValue>
|
||||
<Value>false</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Multiview</Name>
|
||||
<DefaultValue>false</DefaultValue>
|
||||
<Value>false</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>160.000000</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa963f5, 0x1a638cd4)</Identifier>
|
||||
<Value>51</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>224.000000</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x92c056a7, 0x2dc71aff)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x527ad68d, 0x16d746a0)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xad100179, 0xa3c984ab)</Identifier>
|
||||
<Value>88</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>9</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>3</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x0000576e, 0x00000a4e)</Identifier>
|
||||
<Name>Sinus oscillator</Name>
|
||||
<AlgorithmClassIdentifier>(0x7e33bdb8, 0x68194a4a)</AlgorithmClassIdentifier>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Generated signal</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
|
||||
<Name>Channel count</Name>
|
||||
<DefaultValue>4</DefaultValue>
|
||||
<Value>4</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
|
||||
<Name>Sampling frequency</Name>
|
||||
<DefaultValue>512</DefaultValue>
|
||||
<Value>512</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
|
||||
<Name>Generated epoch sample count</Name>
|
||||
<DefaultValue>32</DefaultValue>
|
||||
<Value>32</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>32</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa963f5, 0x1a638cd4)</Identifier>
|
||||
<Value>25</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>304</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x0b214ed8, 0x1f9ad83a)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xad100179, 0xa3c984ab)</Identifier>
|
||||
<Value>121</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x00ac4ab1)</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x00006298, 0x000025dc)</Identifier>
|
||||
<Name>After</Name>
|
||||
<AlgorithmClassIdentifier>(0x0055be5f, 0x087bdd12)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Data</Name>
|
||||
</Input>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
|
||||
<Name>Stimulations</Name>
|
||||
</Input>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x6ab26b81, 0x0f8c02f3)</TypeIdentifier>
|
||||
<Name>Channel Units</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x5de046a6, 0x086340aa)</TypeIdentifier>
|
||||
<Name>Display Mode</Name>
|
||||
<DefaultValue>Scan</DefaultValue>
|
||||
<Value>Scan</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x33a30739, 0x00d5299b)</TypeIdentifier>
|
||||
<Name>Auto vertical scale</Name>
|
||||
<DefaultValue>Per channel</DefaultValue>
|
||||
<Value>Per channel</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Scale refresh interval (secs)</Name>
|
||||
<DefaultValue>5</DefaultValue>
|
||||
<Value>5</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Vertical Scale</Name>
|
||||
<DefaultValue>100</DefaultValue>
|
||||
<Value>100</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Vertical Offset</Name>
|
||||
<DefaultValue>0</DefaultValue>
|
||||
<Value>0</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Time Scale</Name>
|
||||
<DefaultValue>10</DefaultValue>
|
||||
<Value>10</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Horizontal ruler</Name>
|
||||
<DefaultValue>true</DefaultValue>
|
||||
<Value>true</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Vertical ruler</Name>
|
||||
<DefaultValue>false</DefaultValue>
|
||||
<Value>false</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Multiview</Name>
|
||||
<DefaultValue>false</DefaultValue>
|
||||
<Value>false</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>256.000000</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa963f5, 0x1a638cd4)</Identifier>
|
||||
<Value>51</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>304.000000</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x92c056a7, 0x2dc71aff)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x527ad68d, 0x16d746a0)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xad100179, 0xa3c984ab)</Identifier>
|
||||
<Value>88</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc67a01dc, 0x28ce06c1)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>9</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>3</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
</Boxes>
|
||||
<Links>
|
||||
<Link>
|
||||
<Identifier>(0x00000924, 0x00001d39)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x0000576e, 0x00000a4e)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
||||
</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x00000ba1, 0x00004ae1)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
||||
</Target>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1b32c44c, 0x1905e0e9)</Identifier>
|
||||
<Value>51</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x358ae8b5, 0x0f8bacd1)</Identifier>
|
||||
<Value>304</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x3f0a3b27, 0x570913d2)</Identifier>
|
||||
<Value>130</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x6267b5c5, 0x676e3e42)</Identifier>
|
||||
<Value>209</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Link>
|
||||
<Link>
|
||||
<Identifier>(0x00003d0f, 0x000056cd)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x00000503, 0x00001d65)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
||||
</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x00006298, 0x000025dc)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
||||
</Target>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1b32c44c, 0x1905e0e9)</Identifier>
|
||||
<Value>192</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x358ae8b5, 0x0f8bacd1)</Identifier>
|
||||
<Value>336</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x3f0a3b27, 0x570913d2)</Identifier>
|
||||
<Value>226</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x6267b5c5, 0x676e3e42)</Identifier>
|
||||
<Value>289</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Link>
|
||||
<Link>
|
||||
<Identifier>(0x00007953, 0x00007c5f)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x0000576e, 0x00000a4e)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
||||
</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x00000503, 0x00001d65)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
||||
</Target>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1b32c44c, 0x1905e0e9)</Identifier>
|
||||
<Value>51</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x358ae8b5, 0x0f8bacd1)</Identifier>
|
||||
<Value>304</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x3f0a3b27, 0x570913d2)</Identifier>
|
||||
<Value>130</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x6267b5c5, 0x676e3e42)</Identifier>
|
||||
<Value>336</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Link>
|
||||
</Links>
|
||||
<Comments>
|
||||
<Comment>
|
||||
<Identifier>(0x00002f95, 0x0000072c)</Identifier>
|
||||
<Text>You can browse each box' documentation by selecting the box and pressing <b>F1</b></Text>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x473d9a43, 0x97fc0a97)</Identifier>
|
||||
<Value>464</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x7234b86b, 0x2b8651a5)</Identifier>
|
||||
<Value>352</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Comment>
|
||||
<Comment>
|
||||
<Identifier>(0x0000479b, 0x00005f64)</Identifier>
|
||||
<Text>Finally, the right <i>Signal Display</i> box
|
||||
displays the 2 selected channels</Text>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x473d9a43, 0x97fc0a97)</Identifier>
|
||||
<Value>576</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x7234b86b, 0x2b8651a5)</Identifier>
|
||||
<Value>272</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Comment>
|
||||
<Comment>
|
||||
<Identifier>(0x000047d8, 0x00000b37)</Identifier>
|
||||
<Text>The <i><b>Channel Selector</b></i> box takes only
|
||||
the <b>first</b> and <b>fourth</b> channels
|
||||
<small>(channel names are 0 indexed)</small></Text>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x473d9a43, 0x97fc0a97)</Identifier>
|
||||
<Value>576</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x7234b86b, 0x2b8651a5)</Identifier>
|
||||
<Value>192</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Comment>
|
||||
<Comment>
|
||||
<Identifier>(0x00004fd2, 0x00003b1e)</Identifier>
|
||||
<Text>Those 4 channels are displayed with the left
|
||||
<i>Signal Display</i> box</Text>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x473d9a43, 0x97fc0a97)</Identifier>
|
||||
<Value>576</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x7234b86b, 0x2b8651a5)</Identifier>
|
||||
<Value>112</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Comment>
|
||||
<Comment>
|
||||
<Identifier>(0x000057a6, 0x00006a3d)</Identifier>
|
||||
<Text>The <i>Sinus Oscillator</i> box generates
|
||||
a 4 channels sinusoidal signal</Text>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x473d9a43, 0x97fc0a97)</Identifier>
|
||||
<Value>576</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x7234b86b, 0x2b8651a5)</Identifier>
|
||||
<Value>32</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Comment>
|
||||
</Comments>
|
||||
<Metadata>
|
||||
<Entry>
|
||||
<Identifier>(0x0000775c, 0x000078ff)</Identifier>
|
||||
<Type>(0x3bcce5d2, 0x43f2d968)</Type>
|
||||
<Data>[{"boxIdentifier":"(0xffffffff, 0xffffffff)","childCount":1,"height":678,"identifier":"(0x0000037f, 0x000006dc)","index":0,"name":"Default window","parentIdentifier":"(0xffffffff, 0xffffffff)","type":1,"width":794},{"boxIdentifier":"(0xffffffff, 0xffffffff)","childCount":1,"identifier":"(0x00003ddc, 0x00006202)","index":0,"name":"Default tab","parentIdentifier":"(0x0000037f, 0x000006dc)","type":2},{"boxIdentifier":"(0xffffffff, 0xffffffff)","childCount":2,"dividerPosition":314,"identifier":"(0x000019f0, 0x0000358d)","index":0,"maxDividerPosition":633,"name":"Vertical split","parentIdentifier":"(0x00003ddc, 0x00006202)","type":4},{"boxIdentifier":"(0x00000ba1, 0x00004ae1)","childCount":0,"identifier":"(0x0000654d, 0x000076f4)","index":0,"parentIdentifier":"(0x000019f0, 0x0000358d)","type":3},{"boxIdentifier":"(0x00006298, 0x000025dc)","childCount":0,"identifier":"(0x00000303, 0x00002727)","index":1,"parentIdentifier":"(0x000019f0, 0x0000358d)","type":3}]</Data>
|
||||
</Entry>
|
||||
</Metadata>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x790d75b8, 0x3bb90c33)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x8c1fc55b, 0x7b433dc2)</Identifier>
|
||||
<Value>1.0</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x9f5c4075, 0x4a0d3666)</Identifier>
|
||||
<Value>Channel Selector tutorial</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xf36a1567, 0xd13c53da)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xf6b2e3fa, 0x7bd43926)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xf8034a49, 0x8b3f37cc)</Identifier>
|
||||
<Value>Inria</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</OpenViBE-Scenario>
|
||||
+1550
File diff suppressed because it is too large
Load Diff
+509
@@ -0,0 +1,509 @@
|
||||
<OpenViBE-Scenario>
|
||||
<FormatVersion>1</FormatVersion>
|
||||
<Creator>openvibe</Creator>
|
||||
<CreatorVersion>2.0</CreatorVersion>
|
||||
<Boxes>
|
||||
<Box>
|
||||
<Identifier>(0x0000376d, 0x000001ee)</Identifier>
|
||||
<Name>Signal display</Name>
|
||||
<AlgorithmClassIdentifier>(0x0055be5f, 0x087bdd12)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Data</Name>
|
||||
</Input>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
|
||||
<Name>Stimulations</Name>
|
||||
</Input>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x6ab26b81, 0x0f8c02f3)</TypeIdentifier>
|
||||
<Name>Channel Units</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x5de046a6, 0x086340aa)</TypeIdentifier>
|
||||
<Name>Display Mode</Name>
|
||||
<DefaultValue>Scan</DefaultValue>
|
||||
<Value>Scan</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x33a30739, 0x00d5299b)</TypeIdentifier>
|
||||
<Name>Auto vertical scale</Name>
|
||||
<DefaultValue>Per channel</DefaultValue>
|
||||
<Value>Per channel</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Scale refresh interval (secs)</Name>
|
||||
<DefaultValue>5</DefaultValue>
|
||||
<Value>5</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Vertical Scale</Name>
|
||||
<DefaultValue>100</DefaultValue>
|
||||
<Value>100</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Vertical Offset</Name>
|
||||
<DefaultValue>0</DefaultValue>
|
||||
<Value>0</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Time Scale</Name>
|
||||
<DefaultValue>10</DefaultValue>
|
||||
<Value>10</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Horizontal ruler</Name>
|
||||
<DefaultValue>true</DefaultValue>
|
||||
<Value>true</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Vertical ruler</Name>
|
||||
<DefaultValue>false</DefaultValue>
|
||||
<Value>false</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Multiview</Name>
|
||||
<DefaultValue>false</DefaultValue>
|
||||
<Value>false</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>336.000000</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa963f5, 0x1a638cd4)</Identifier>
|
||||
<Value>38</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>304.000000</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x92c056a7, 0x2dc71aff)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x527ad68d, 0x16d746a0)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xad100179, 0xa3c984ab)</Identifier>
|
||||
<Value>113</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc67a01dc, 0x28ce06c1)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>9</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>3</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x5bdd9691, 0x130584b9)</Identifier>
|
||||
<Name>Crop</Name>
|
||||
<AlgorithmClassIdentifier>(0x7f1a3002, 0x358117ba)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Input matrix</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Output matrix</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0xd0643f9e, 0x8e35fe0a)</TypeIdentifier>
|
||||
<Name>Crop method</Name>
|
||||
<DefaultValue>Min</DefaultValue>
|
||||
<Value>Min/Max</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Min crop value</Name>
|
||||
<DefaultValue>-1</DefaultValue>
|
||||
<Value>-3.000000</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Max crop value</Name>
|
||||
<DefaultValue>1</DefaultValue>
|
||||
<Value>3.000000</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>224</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa963f5, 0x1a638cd4)</Identifier>
|
||||
<Value>38</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>320</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x30a4e5c9, 0x83502953)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x1b151919, 0x63b9f9c9)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x527ad68d, 0x16d746a0)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xad100179, 0xa3c984ab)</Identifier>
|
||||
<Value>67</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x005c9c00)</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x5e2124c7, 0x1bda2228)</Identifier>
|
||||
<Name>Simple DSP</Name>
|
||||
<AlgorithmClassIdentifier>(0x00e26fa1, 0x1dbab1b2)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Input - A</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Output</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x79a9edeb, 0x245d83fc)</TypeIdentifier>
|
||||
<Name>Equation</Name>
|
||||
<DefaultValue>x</DefaultValue>
|
||||
<Value>4 * cos(X)</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>112</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa963f5, 0x1a638cd4)</Identifier>
|
||||
<Value>38</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>320</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x30a4e5c9, 0x83502953)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x21889dc4, 0x1126497e)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x527ad68d, 0x16d746a0)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xad100179, 0xa3c984ab)</Identifier>
|
||||
<Value>95</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xfba64161, 0x65304e21)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x791a5334, 0x05cb62a2)</Identifier>
|
||||
<Name>Time signal</Name>
|
||||
<AlgorithmClassIdentifier>(0x28a5e7ff, 0x530095de)</AlgorithmClassIdentifier>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Generated signal</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
|
||||
<Name>Sampling frequency</Name>
|
||||
<DefaultValue>512</DefaultValue>
|
||||
<Value>512</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
|
||||
<Name>Generated epoch sample count</Name>
|
||||
<DefaultValue>32</DefaultValue>
|
||||
<Value>32</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>32</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa963f5, 0x1a638cd4)</Identifier>
|
||||
<Value>25</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>320</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x9e5ca01e, 0x30a4d8c3)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xad100179, 0xa3c984ab)</Identifier>
|
||||
<Value>94</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x00903800)</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
</Boxes>
|
||||
<Links>
|
||||
<Link>
|
||||
<Identifier>(0x00002232, 0x000023c9)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x5bdd9691, 0x130584b9)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
||||
</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x0000376d, 0x000001ee)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
||||
</Target>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1b32c44c, 0x1905e0e9)</Identifier>
|
||||
<Value>249</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x358ae8b5, 0x0f8bacd1)</Identifier>
|
||||
<Value>320</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x3f0a3b27, 0x570913d2)</Identifier>
|
||||
<Value>312</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x6267b5c5, 0x676e3e42)</Identifier>
|
||||
<Value>289</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Link>
|
||||
<Link>
|
||||
<Identifier>(0x39ec49f3, 0x56c136ff)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x791a5334, 0x05cb62a2)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
||||
</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x5e2124c7, 0x1bda2228)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
||||
</Target>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1b32c44c, 0x1905e0e9)</Identifier>
|
||||
<Value>51</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x358ae8b5, 0x0f8bacd1)</Identifier>
|
||||
<Value>320</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x3f0a3b27, 0x570913d2)</Identifier>
|
||||
<Value>88</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x6267b5c5, 0x676e3e42)</Identifier>
|
||||
<Value>320</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Link>
|
||||
<Link>
|
||||
<Identifier>(0x3f52384e, 0x2c2082d2)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x5e2124c7, 0x1bda2228)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
||||
</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x5bdd9691, 0x130584b9)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
||||
</Target>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1b32c44c, 0x1905e0e9)</Identifier>
|
||||
<Value>137</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x358ae8b5, 0x0f8bacd1)</Identifier>
|
||||
<Value>320</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x3f0a3b27, 0x570913d2)</Identifier>
|
||||
<Value>200</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x6267b5c5, 0x676e3e42)</Identifier>
|
||||
<Value>320</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Link>
|
||||
</Links>
|
||||
<Comments>
|
||||
<Comment>
|
||||
<Identifier>(0x071dad5e, 0x17b33a03)</Identifier>
|
||||
<Text>The <i>Time Signal</i> box generates
|
||||
a 1 channel linear signal (<i>f(t)=t</i>)</Text>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x473d9a43, 0x97fc0a97)</Identifier>
|
||||
<Value>576</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x7234b86b, 0x2b8651a5)</Identifier>
|
||||
<Value>48</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Comment>
|
||||
<Comment>
|
||||
<Identifier>(0x0f97f08c, 0x14474c97)</Identifier>
|
||||
<Text>The <i>Simple DSP</i> box applies a simple
|
||||
function to each sample. This function is
|
||||
<i>4 * cos(X)</i> so the output signal is a
|
||||
sinusoid in the [-4 +4] range.</Text>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x473d9a43, 0x97fc0a97)</Identifier>
|
||||
<Value>576</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x7234b86b, 0x2b8651a5)</Identifier>
|
||||
<Value>128</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Comment>
|
||||
<Comment>
|
||||
<Identifier>(0x612a81a4, 0x68d7e635)</Identifier>
|
||||
<Text>You can browse each box' documentation by selecting the box and pressing <b>F1</b></Text>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x473d9a43, 0x97fc0a97)</Identifier>
|
||||
<Value>480</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x7234b86b, 0x2b8651a5)</Identifier>
|
||||
<Value>400</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Comment>
|
||||
<Comment>
|
||||
<Identifier>(0x698cfa37, 0x232575e8)</Identifier>
|
||||
<Text>The <i><b>Crop</b></i> box cuts the signal
|
||||
to a minimum of <b>-3</b> and a maximum of
|
||||
<b>+3</b>. If the signal gets lower to the minimum
|
||||
or higher to the maximum, the sample value is simply
|
||||
replaced by the actual minimum or the actual maximum.</Text>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x473d9a43, 0x97fc0a97)</Identifier>
|
||||
<Value>576</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x7234b86b, 0x2b8651a5)</Identifier>
|
||||
<Value>240</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Comment>
|
||||
<Comment>
|
||||
<Identifier>(0x7278a690, 0x0b27f24e)</Identifier>
|
||||
<Text>Finally, the <i>Signal Display</i> box
|
||||
displays the result.</Text>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x473d9a43, 0x97fc0a97)</Identifier>
|
||||
<Value>576</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x7234b86b, 0x2b8651a5)</Identifier>
|
||||
<Value>336</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Comment>
|
||||
</Comments>
|
||||
<Metadata>
|
||||
<Entry>
|
||||
<Identifier>(0x0000775c, 0x000078ff)</Identifier>
|
||||
<Type>(0x3bcce5d2, 0x43f2d968)</Type>
|
||||
<Data>[{"boxIdentifier":"(0x0000376d, 0x000001ee)","childCount":0,"identifier":"(0x0000688d, 0x00006cbf)","index":0,"parentIdentifier":"(0xffffffff, 0xffffffff)","type":3},{"boxIdentifier":"(0xffffffff, 0xffffffff)","childCount":1,"height":1,"identifier":"(0x1aaf4e4d, 0x2e459ad4)","index":0,"name":"Default window","parentIdentifier":"(0xffffffff, 0xffffffff)","type":1,"width":1},{"boxIdentifier":"(0xffffffff, 0xffffffff)","childCount":1,"identifier":"(0x7293e89a, 0x29495377)","index":0,"name":"Default tab","parentIdentifier":"(0x1aaf4e4d, 0x2e459ad4)","type":2},{"boxIdentifier":"(0xffffffff, 0xffffffff)","childCount":0,"identifier":"(0x2b68cb14, 0x3a1b7595)","index":0,"name":"Empty","parentIdentifier":"(0x7293e89a, 0x29495377)","type":0}]</Data>
|
||||
</Entry>
|
||||
</Metadata>
|
||||
</OpenViBE-Scenario>
|
||||
+17
@@ -0,0 +1,17 @@
|
||||
4 4 32
|
||||
0.190954
|
||||
0.0934296
|
||||
-0.018267
|
||||
0.43378
|
||||
0.0588937
|
||||
0.28019
|
||||
-0.0240636
|
||||
0.308092
|
||||
0.699474
|
||||
-0.0584318
|
||||
-0.0255184
|
||||
0.189858
|
||||
0.224142
|
||||
0.139218
|
||||
-0.0306152
|
||||
0.215256
|
||||
+916
@@ -0,0 +1,916 @@
|
||||
<OpenViBE-Scenario>
|
||||
<FormatVersion>2</FormatVersion>
|
||||
<Creator>OpenViBE Designer</Creator>
|
||||
<CreatorVersion>2.1.0</CreatorVersion>
|
||||
<Settings></Settings>
|
||||
<Inputs></Inputs>
|
||||
<Outputs></Outputs>
|
||||
<Boxes>
|
||||
<Box>
|
||||
<Identifier>(0x000018a8, 0x00007deb)</Identifier>
|
||||
<Name>Keyboard stimulator</Name>
|
||||
<AlgorithmClassIdentifier>(0x00d317b9, 0x6324c3ff)</AlgorithmClassIdentifier>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
|
||||
<Name>Outgoing Stimulations</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x330306dd, 0x74a95f98)</TypeIdentifier>
|
||||
<Name>Filename</Name>
|
||||
<DefaultValue>${Path_Data}/plugins/stimulation/simple-keyboard-to-stimulations.txt</DefaultValue>
|
||||
<Value>${Path_Data}/plugins/stimulation/simple-keyboard-to-stimulations.txt</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>192</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>208</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x9b5dd008, 0x475a2ecd)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x027a6071)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x00001ac2, 0x00007b76)</Identifier>
|
||||
<Name>GDF file reader</Name>
|
||||
<AlgorithmClassIdentifier>(0x3eeb1264, 0x4edfbd9a)</AlgorithmClassIdentifier>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x403488e7, 0x565d70b6)</TypeIdentifier>
|
||||
<Name>Experiment information</Name>
|
||||
</Output>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>EEG stream</Name>
|
||||
</Output>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
|
||||
<Name>Stimulations</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x330306dd, 0x74a95f98)</TypeIdentifier>
|
||||
<Name>Filename</Name>
|
||||
<DefaultValue></DefaultValue>
|
||||
<Value>${Path_Data}/scenarios/signals/eog-artifact.gdf</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
|
||||
<Name>Samples per buffer</Name>
|
||||
<DefaultValue>32</DefaultValue>
|
||||
<Value>32</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Subtract physical minimum</Name>
|
||||
<DefaultValue>False</DefaultValue>
|
||||
<Value>False</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>-48</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>385</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x78b8b69d, 0x27afe678)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x016d098c)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
||||
<Value>3</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>3</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x00001ccf, 0x00002414)</Identifier>
|
||||
<Name>EOG</Name>
|
||||
<AlgorithmClassIdentifier>(0x0055be5f, 0x087bdd12)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Data</Name>
|
||||
</Input>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
|
||||
<Name>Stimulations</Name>
|
||||
</Input>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x6ab26b81, 0x0f8c02f3)</TypeIdentifier>
|
||||
<Name>Channel Units</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x5de046a6, 0x086340aa)</TypeIdentifier>
|
||||
<Name>Display Mode</Name>
|
||||
<DefaultValue>Scan</DefaultValue>
|
||||
<Value>Scan</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x33a30739, 0x00d5299b)</TypeIdentifier>
|
||||
<Name>Auto vertical scale</Name>
|
||||
<DefaultValue>Per channel</DefaultValue>
|
||||
<Value>Per channel</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Scale refresh interval (secs)</Name>
|
||||
<DefaultValue>5</DefaultValue>
|
||||
<Value>5</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Vertical Scale</Name>
|
||||
<DefaultValue>100</DefaultValue>
|
||||
<Value>100</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Vertical Offset</Name>
|
||||
<DefaultValue>0</DefaultValue>
|
||||
<Value>0</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Time Scale</Name>
|
||||
<DefaultValue>10</DefaultValue>
|
||||
<Value>10</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Horizontal ruler</Name>
|
||||
<DefaultValue>true</DefaultValue>
|
||||
<Value>true</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Vertical ruler</Name>
|
||||
<DefaultValue>false</DefaultValue>
|
||||
<Value>false</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Multiview</Name>
|
||||
<DefaultValue>false</DefaultValue>
|
||||
<Value>false</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>448</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>224</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x92c056a7, 0x2dc71aff)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x527ad68d, 0x16d746a0)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>9</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>3</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x00003b19, 0x00002e65)</Identifier>
|
||||
<Name>EOG</Name>
|
||||
<AlgorithmClassIdentifier>(0x361722e8, 0x311574e8)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Input signal</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Output signal</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x79a9edeb, 0x245d83fc)</TypeIdentifier>
|
||||
<Name>Channel List</Name>
|
||||
<DefaultValue>:</DefaultValue>
|
||||
<Value>1:4</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x3bcf9e67, 0x0c23994d)</TypeIdentifier>
|
||||
<Name>Action</Name>
|
||||
<DefaultValue>Select</DefaultValue>
|
||||
<Value>Select</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x666f25e9, 0x3e5738d6)</TypeIdentifier>
|
||||
<Name>Channel Matching Method</Name>
|
||||
<DefaultValue>Smart</DefaultValue>
|
||||
<Value>Smart</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>128</Value>
|
||||
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|
||||
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|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>448</Value>
|
||||
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|
||||
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|
||||
<Identifier>(0x30a4e5c9, 0x83502953)</Identifier>
|
||||
<Value></Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x277826e1, 0xa30a3bd0)</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0x527ad68d, 0x16d746a0)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x001ceae3)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>3</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x0000416e, 0x00001e7e)</Identifier>
|
||||
<Name>EOG_Denoising_Calibration</Name>
|
||||
<AlgorithmClassIdentifier>(0xe8dfe002, 0x70389932)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>EEG</Name>
|
||||
</Input>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>EOG</Name>
|
||||
</Input>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
|
||||
<Name>Stimulations</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
|
||||
<Name>Train-completed Flag</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x330306dd, 0x74a95f98)</TypeIdentifier>
|
||||
<Name>Filename b Matrix</Name>
|
||||
<DefaultValue>b-Matrix-EEG.cfg</DefaultValue>
|
||||
<Value>${Player_ScenarioDirectory}/b-Matrix-EEG.cfg</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2c132d6e, 0x44ab0d97)</TypeIdentifier>
|
||||
<Name>Train Trigger</Name>
|
||||
<DefaultValue>OVTK_GDF_End_Of_Session</DefaultValue>
|
||||
<Value>OVTK_GDF_End_Of_Session</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>289</Value>
|
||||
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|
||||
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|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>384</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0xefef60c9, 0xe53f77fc)</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x02afc5fc)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>2</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>3</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x000041f0, 0x00007465)</Identifier>
|
||||
<Name>EEG</Name>
|
||||
<AlgorithmClassIdentifier>(0x0055be5f, 0x087bdd12)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Data</Name>
|
||||
</Input>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
|
||||
<Name>Stimulations</Name>
|
||||
</Input>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x6ab26b81, 0x0f8c02f3)</TypeIdentifier>
|
||||
<Name>Channel Units</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x5de046a6, 0x086340aa)</TypeIdentifier>
|
||||
<Name>Display Mode</Name>
|
||||
<DefaultValue>Scan</DefaultValue>
|
||||
<Value>Scan</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x33a30739, 0x00d5299b)</TypeIdentifier>
|
||||
<Name>Auto vertical scale</Name>
|
||||
<DefaultValue>Per channel</DefaultValue>
|
||||
<Value>Per channel</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Scale refresh interval (secs)</Name>
|
||||
<DefaultValue>5</DefaultValue>
|
||||
<Value>5</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Vertical Scale</Name>
|
||||
<DefaultValue>100</DefaultValue>
|
||||
<Value>100</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Vertical Offset</Name>
|
||||
<DefaultValue>0</DefaultValue>
|
||||
<Value>0</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Time Scale</Name>
|
||||
<DefaultValue>10</DefaultValue>
|
||||
<Value>10</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Horizontal ruler</Name>
|
||||
<DefaultValue>true</DefaultValue>
|
||||
<Value>true</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Vertical ruler</Name>
|
||||
<DefaultValue>false</DefaultValue>
|
||||
<Value>false</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Multiview</Name>
|
||||
<DefaultValue>false</DefaultValue>
|
||||
<Value>false</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>448</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>96</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x92c056a7, 0x2dc71aff)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x527ad68d, 0x16d746a0)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>9</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>3</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x0000489a, 0x0000011d)</Identifier>
|
||||
<Name>Player Controller</Name>
|
||||
<AlgorithmClassIdentifier>(0x5f426dce, 0x08456e13)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
|
||||
<Name>Stimulations</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2c132d6e, 0x44ab0d97)</TypeIdentifier>
|
||||
<Name>Stimulation name</Name>
|
||||
<DefaultValue>OVTK_StimulationId_Label_00</DefaultValue>
|
||||
<Value>OVTK_StimulationId_TrainCompleted</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0xcc14d8d6, 0xf27ecb73)</TypeIdentifier>
|
||||
<Name>Action to perform</Name>
|
||||
<DefaultValue>Pause</DefaultValue>
|
||||
<Value>Stop</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>400</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>352</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x568d148e, 0x650792b3)</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x00f1bfbf)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>2</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x00006eb3, 0x00003bc9)</Identifier>
|
||||
<Name>Temporal filter</Name>
|
||||
<AlgorithmClassIdentifier>(0xb4f9d042, 0x9d79f2e5)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Input signal</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Filtered signal</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2f2c606c, 0x8512ed68)</TypeIdentifier>
|
||||
<Name>Filter method</Name>
|
||||
<DefaultValue>Butterworth</DefaultValue>
|
||||
<Value>Butterworth</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0xfa20178e, 0x4cba62e9)</TypeIdentifier>
|
||||
<Name>Filter type</Name>
|
||||
<DefaultValue>Band pass</DefaultValue>
|
||||
<Value>Band pass</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
|
||||
<Name>Filter order</Name>
|
||||
<DefaultValue>4</DefaultValue>
|
||||
<Value>4</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Low cut frequency (Hz)</Name>
|
||||
<DefaultValue>29</DefaultValue>
|
||||
<Value>2</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>High cut frequency (Hz)</Name>
|
||||
<DefaultValue>40</DefaultValue>
|
||||
<Value>40</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Pass band ripple (dB)</Name>
|
||||
<DefaultValue>0.5</DefaultValue>
|
||||
<Value>0.5</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
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<Value>32</Value>
|
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</Attribute>
|
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<Attribute>
|
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<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>384</Value>
|
||||
</Attribute>
|
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<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x27a4ceec, 0x876d6384)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
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<Value>(0x00000000, 0x01129423)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
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<Attribute>
|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
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<Value>1</Value>
|
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</Attribute>
|
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<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
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<Value>6</Value>
|
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</Attribute>
|
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<Attribute>
|
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<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
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<Value>1</Value>
|
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</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x000071e4, 0x00006ad6)</Identifier>
|
||||
<Name>EEG</Name>
|
||||
<AlgorithmClassIdentifier>(0x361722e8, 0x311574e8)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Input signal</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Output signal</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x79a9edeb, 0x245d83fc)</TypeIdentifier>
|
||||
<Name>Channel List</Name>
|
||||
<DefaultValue>:</DefaultValue>
|
||||
<Value>1:4</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x3bcf9e67, 0x0c23994d)</TypeIdentifier>
|
||||
<Name>Action</Name>
|
||||
<DefaultValue>Select</DefaultValue>
|
||||
<Value>Reject</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x666f25e9, 0x3e5738d6)</TypeIdentifier>
|
||||
<Name>Channel Matching Method</Name>
|
||||
<DefaultValue>Smart</DefaultValue>
|
||||
<Value>Smart</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>128</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>320</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x30a4e5c9, 0x83502953)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x277826e1, 0xa30a3bd0)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x527ad68d, 0x16d746a0)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
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<Attribute>
|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x00269e1b)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>3</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
</Boxes>
|
||||
<Links>
|
||||
<Link>
|
||||
<Identifier>(0x000022d8, 0x00002198)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x00003b19, 0x00002e65)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
||||
</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x0000416e, 0x00001e7e)</BoxIdentifier>
|
||||
<BoxInputIndex>1</BoxInputIndex>
|
||||
</Target>
|
||||
</Link>
|
||||
<Link>
|
||||
<Identifier>(0x00002618, 0x00005d65)</Identifier>
|
||||
<Source>
|
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<BoxIdentifier>(0x000071e4, 0x00006ad6)</BoxIdentifier>
|
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<BoxOutputIndex>0</BoxOutputIndex>
|
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</Source>
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||||
<Target>
|
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<BoxIdentifier>(0x000041f0, 0x00007465)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
||||
</Target>
|
||||
</Link>
|
||||
<Link>
|
||||
<Identifier>(0x000026a2, 0x00001e65)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x00006eb3, 0x00003bc9)</BoxIdentifier>
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||||
<BoxOutputIndex>0</BoxOutputIndex>
|
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</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x00003b19, 0x00002e65)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
||||
</Target>
|
||||
</Link>
|
||||
<Link>
|
||||
<Identifier>(0x00002eaa, 0x00005a2b)</Identifier>
|
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<Source>
|
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<BoxIdentifier>(0x00001ac2, 0x00007b76)</BoxIdentifier>
|
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<BoxOutputIndex>1</BoxOutputIndex>
|
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</Source>
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<Target>
|
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<BoxIdentifier>(0x00006eb3, 0x00003bc9)</BoxIdentifier>
|
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<BoxInputIndex>0</BoxInputIndex>
|
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</Target>
|
||||
</Link>
|
||||
<Link>
|
||||
<Identifier>(0x000039a0, 0x000077bd)</Identifier>
|
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<Source>
|
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<BoxIdentifier>(0x000018a8, 0x00007deb)</BoxIdentifier>
|
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<BoxOutputIndex>0</BoxOutputIndex>
|
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</Source>
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<Target>
|
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<BoxIdentifier>(0x0000416e, 0x00001e7e)</BoxIdentifier>
|
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<BoxInputIndex>2</BoxInputIndex>
|
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</Target>
|
||||
</Link>
|
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<Link>
|
||||
<Identifier>(0x000049ef, 0x000056c3)</Identifier>
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<Source>
|
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|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
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</Source>
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<Target>
|
||||
<BoxIdentifier>(0x00001ccf, 0x00002414)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
||||
</Target>
|
||||
</Link>
|
||||
<Link>
|
||||
<Identifier>(0x00005978, 0x0000666f)</Identifier>
|
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<Source>
|
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<BoxIdentifier>(0x00006eb3, 0x00003bc9)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
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</Source>
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|
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<BoxIdentifier>(0x000071e4, 0x00006ad6)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
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</Target>
|
||||
</Link>
|
||||
<Link>
|
||||
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|
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<Source>
|
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|
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<BoxOutputIndex>0</BoxOutputIndex>
|
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</Source>
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<Target>
|
||||
<BoxIdentifier>(0x0000489a, 0x0000011d)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
||||
</Target>
|
||||
</Link>
|
||||
<Link>
|
||||
<Identifier>(0x00007c20, 0x00000003)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x000071e4, 0x00006ad6)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
||||
</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x0000416e, 0x00001e7e)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
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</Target>
|
||||
</Link>
|
||||
</Links>
|
||||
<Comments>
|
||||
<Comment>
|
||||
<Identifier>(0x00001b37, 0x000073e5)</Identifier>
|
||||
<Text>This scenario estimates a denoising regressor
|
||||
following the work of Schlögl and al., 2007.
|
||||
|
||||
Here, <b><i>EOG Denoising Calibration</i></b> estimates
|
||||
the model using a more contaminated signal in the EOG
|
||||
input and the rest (EEG signal) in the EEG input.
|
||||
|
||||
Press 'a' to in <b><i>Keyboard Stimulator</i></b> to start the
|
||||
model estimation. Press 'u' to stop. The model is
|
||||
output after the whole input has been processed.
|
||||
|
||||
After the model has been estimated, it can be
|
||||
used with the 'eog-run.xml' scenario.
|
||||
|
||||
|
||||
</Text>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x473d9a43, 0x97fc0a97)</Identifier>
|
||||
<Value>720.000000</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x7234b86b, 0x2b8651a5)</Identifier>
|
||||
<Value>160.000000</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Comment>
|
||||
</Comments>
|
||||
<Metadata>
|
||||
<Entry>
|
||||
<Identifier>(0x0000775c, 0x000078ff)</Identifier>
|
||||
<Type>(0x3bcce5d2, 0x43f2d968)</Type>
|
||||
<Data>[{"boxIdentifier":"(0x000041f0, 0x00007465)","childCount":0,"identifier":"(0x0000218f, 0x00003d69)","parentIdentifier":"(0xffffffff, 0xffffffff)","type":3},{"boxIdentifier":"(0xffffffff, 0xffffffff)","childCount":1,"height":320,"identifier":"(0x0000299d, 0x00003e7a)","name":"Default window","parentIdentifier":"(0xffffffff, 0xffffffff)","type":1,"width":475},{"boxIdentifier":"(0x000018a8, 0x00007deb)","childCount":0,"identifier":"(0x000047a3, 0x0000671a)","parentIdentifier":"(0xffffffff, 0xffffffff)","type":3},{"boxIdentifier":"(0x00001ccf, 0x00002414)","childCount":0,"identifier":"(0x00005019, 0x00007d87)","parentIdentifier":"(0xffffffff, 0xffffffff)","type":3},{"boxIdentifier":"(0xffffffff, 0xffffffff)","childCount":1,"identifier":"(0x000011ab, 0x00002e7d)","index":0,"name":"Default tab","parentIdentifier":"(0x0000299d, 0x00003e7a)","type":2},{"boxIdentifier":"(0xffffffff, 0xffffffff)","childCount":0,"identifier":"(0x0000031e, 0x000026a1)","index":0,"name":"Empty","parentIdentifier":"(0x000011ab, 0x00002e7d)","type":0}]</Data>
|
||||
</Entry>
|
||||
</Metadata>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x790d75b8, 0x3bb90c33)</Identifier>
|
||||
<Value>Joao-Pedro Berti-Ligabo</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x8c1fc55b, 0x7b433dc2)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x9f5c4075, 0x4a0d3666)</Identifier>
|
||||
<Value>EOG: Run</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xf36a1567, 0xd13c53da)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xf6b2e3fa, 0x7bd43926)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xf8034a49, 0x8b3f37cc)</Identifier>
|
||||
<Value>Inria</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</OpenViBE-Scenario>
|
||||
+960
@@ -0,0 +1,960 @@
|
||||
<OpenViBE-Scenario>
|
||||
<FormatVersion>1</FormatVersion>
|
||||
<Creator>openvibe</Creator>
|
||||
<CreatorVersion>2.0</CreatorVersion>
|
||||
<Boxes>
|
||||
<Box>
|
||||
<Identifier>(0x00003b19, 0x00002e65)</Identifier>
|
||||
<Name>EOG</Name>
|
||||
<AlgorithmClassIdentifier>(0x361722e8, 0x311574e8)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Input signal</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Output signal</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x79a9edeb, 0x245d83fc)</TypeIdentifier>
|
||||
<Name>Channel List</Name>
|
||||
<DefaultValue>:</DefaultValue>
|
||||
<Value>1:4</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x3bcf9e67, 0x0c23994d)</TypeIdentifier>
|
||||
<Name>Action</Name>
|
||||
<DefaultValue>Select</DefaultValue>
|
||||
<Value>Select</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x666f25e9, 0x3e5738d6)</TypeIdentifier>
|
||||
<Name>Channel Matching Method</Name>
|
||||
<DefaultValue>Smart</DefaultValue>
|
||||
<Value>Smart</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>192.000000</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa963f5, 0x1a638cd4)</Identifier>
|
||||
<Value>51</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>464.000000</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x30a4e5c9, 0x83502953)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x277826e1, 0xa30a3bd0)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x527ad68d, 0x16d746a0)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xad100179, 0xa3c984ab)</Identifier>
|
||||
<Value>106</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x001ceae3)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>3</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x00004d05, 0x00007de5)</Identifier>
|
||||
<Name>After</Name>
|
||||
<AlgorithmClassIdentifier>(0x0055be5f, 0x087bdd12)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Data</Name>
|
||||
</Input>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
|
||||
<Name>Stimulations</Name>
|
||||
</Input>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x6ab26b81, 0x0f8c02f3)</TypeIdentifier>
|
||||
<Name>Channel Units</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x5de046a6, 0x086340aa)</TypeIdentifier>
|
||||
<Name>Display Mode</Name>
|
||||
<DefaultValue>Scan</DefaultValue>
|
||||
<Value>Scan</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x33a30739, 0x00d5299b)</TypeIdentifier>
|
||||
<Name>Auto vertical scale</Name>
|
||||
<DefaultValue>Per channel</DefaultValue>
|
||||
<Value>Per channel</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Scale refresh interval (secs)</Name>
|
||||
<DefaultValue>5</DefaultValue>
|
||||
<Value>5</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Vertical Scale</Name>
|
||||
<DefaultValue>100</DefaultValue>
|
||||
<Value>100</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Vertical Offset</Name>
|
||||
<DefaultValue>0</DefaultValue>
|
||||
<Value>0</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Time Scale</Name>
|
||||
<DefaultValue>10</DefaultValue>
|
||||
<Value>10</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Horizontal ruler</Name>
|
||||
<DefaultValue>true</DefaultValue>
|
||||
<Value>true</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Vertical ruler</Name>
|
||||
<DefaultValue>false</DefaultValue>
|
||||
<Value>false</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Multiview</Name>
|
||||
<DefaultValue>false</DefaultValue>
|
||||
<Value>false</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>432.000000</Value>
|
||||
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|
||||
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|
||||
<Identifier>(0x1fa963f5, 0x1a638cd4)</Identifier>
|
||||
<Value>51</Value>
|
||||
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|
||||
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|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>416.000000</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x92c056a7, 0x2dc71aff)</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0x527ad68d, 0x16d746a0)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xad100179, 0xa3c984ab)</Identifier>
|
||||
<Value>88</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x00221c5f)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc67a01dc, 0x28ce06c1)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>9</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>3</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x000053ed, 0x0000612d)</Identifier>
|
||||
<Name>EOG Denoising</Name>
|
||||
<AlgorithmClassIdentifier>(0xc223ff12, 0x069a987e)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>EEG</Name>
|
||||
</Input>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>EOG</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>EEG_Corrected</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x330306dd, 0x74a95f98)</TypeIdentifier>
|
||||
<Name>Filename b Matrix</Name>
|
||||
<DefaultValue>b-Matrix-EEG.txt</DefaultValue>
|
||||
<Value>${Player_ScenarioDirectory}/b-Matrix-EEG.cfg</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>288.000000</Value>
|
||||
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|
||||
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|
||||
<Identifier>(0x1fa963f5, 0x1a638cd4)</Identifier>
|
||||
<Value>38</Value>
|
||||
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|
||||
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|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>416.000000</Value>
|
||||
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|
||||
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|
||||
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|
||||
<Value>(0xc15f2638, 0x928d2db0)</Value>
|
||||
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|
||||
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|
||||
<Identifier>(0xad100179, 0xa3c984ab)</Identifier>
|
||||
<Value>119</Value>
|
||||
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|
||||
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|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x002fcaa5)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
||||
<Value>1</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>1</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>2</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x00005e30, 0x0000017a)</Identifier>
|
||||
<Name>GDF file reader</Name>
|
||||
<AlgorithmClassIdentifier>(0x3eeb1264, 0x4edfbd9a)</AlgorithmClassIdentifier>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x403488e7, 0x565d70b6)</TypeIdentifier>
|
||||
<Name>Experiment information</Name>
|
||||
</Output>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>EEG stream</Name>
|
||||
</Output>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
|
||||
<Name>Stimulations</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x330306dd, 0x74a95f98)</TypeIdentifier>
|
||||
<Name>Filename</Name>
|
||||
<DefaultValue></DefaultValue>
|
||||
<Value>${Path_Data}/scenarios/signals/eog-artifact.gdf</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
|
||||
<Name>Samples per buffer</Name>
|
||||
<DefaultValue>32</DefaultValue>
|
||||
<Value>32</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Subtract physical minimum</Name>
|
||||
<DefaultValue>False</DefaultValue>
|
||||
<Value>False</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>32.000000</Value>
|
||||
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|
||||
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|
||||
<Identifier>(0x1fa963f5, 0x1a638cd4)</Identifier>
|
||||
<Value>25</Value>
|
||||
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|
||||
<Attribute>
|
||||
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|
||||
<Value>400.000000</Value>
|
||||
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|
||||
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|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x78b8b69d, 0x27afe678)</Value>
|
||||
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|
||||
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|
||||
<Identifier>(0xad100179, 0xa3c984ab)</Identifier>
|
||||
<Value>123</Value>
|
||||
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|
||||
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|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x00315309)</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
||||
<Value>3</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>3</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x000069dc, 0x00007101)</Identifier>
|
||||
<Name>Temporal filter</Name>
|
||||
<AlgorithmClassIdentifier>(0xb4f9d042, 0x9d79f2e5)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Input signal</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Filtered signal</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2f2c606c, 0x8512ed68)</TypeIdentifier>
|
||||
<Name>Filter method</Name>
|
||||
<DefaultValue>Butterworth</DefaultValue>
|
||||
<Value>Butterworth</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0xfa20178e, 0x4cba62e9)</TypeIdentifier>
|
||||
<Name>Filter type</Name>
|
||||
<DefaultValue>Band pass</DefaultValue>
|
||||
<Value>Band pass</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
|
||||
<Name>Filter order</Name>
|
||||
<DefaultValue>4</DefaultValue>
|
||||
<Value>4</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Low cut frequency (Hz)</Name>
|
||||
<DefaultValue>29</DefaultValue>
|
||||
<Value>2</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>High cut frequency (Hz)</Name>
|
||||
<DefaultValue>40</DefaultValue>
|
||||
<Value>40</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Pass band ripple (dB)</Name>
|
||||
<DefaultValue>0.5</DefaultValue>
|
||||
<Value>0.5</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
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|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>96</Value>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<Identifier>(0xad100179, 0xa3c984ab)</Identifier>
|
||||
<Value>119</Value>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
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|
||||
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|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
||||
<Value>1</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>6</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>1</Value>
|
||||
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|
||||
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|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x000071e4, 0x00006ad6)</Identifier>
|
||||
<Name>EEG</Name>
|
||||
<AlgorithmClassIdentifier>(0x361722e8, 0x311574e8)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Input signal</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Output signal</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x79a9edeb, 0x245d83fc)</TypeIdentifier>
|
||||
<Name>Channel List</Name>
|
||||
<DefaultValue>:</DefaultValue>
|
||||
<Value>1:4</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x3bcf9e67, 0x0c23994d)</TypeIdentifier>
|
||||
<Name>Action</Name>
|
||||
<DefaultValue>Select</DefaultValue>
|
||||
<Value>Reject</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x666f25e9, 0x3e5738d6)</TypeIdentifier>
|
||||
<Name>Channel Matching Method</Name>
|
||||
<DefaultValue>Smart</DefaultValue>
|
||||
<Value>Smart</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
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|
||||
<Attribute>
|
||||
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|
||||
<Value>192.000000</Value>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<Identifier>(0xad100179, 0xa3c984ab)</Identifier>
|
||||
<Value>106</Value>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
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|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
||||
<Value>1</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>3</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x0000721d, 0x00006b03)</Identifier>
|
||||
<Name>Before</Name>
|
||||
<AlgorithmClassIdentifier>(0x0055be5f, 0x087bdd12)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Data</Name>
|
||||
</Input>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
|
||||
<Name>Stimulations</Name>
|
||||
</Input>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x6ab26b81, 0x0f8c02f3)</TypeIdentifier>
|
||||
<Name>Channel Units</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x5de046a6, 0x086340aa)</TypeIdentifier>
|
||||
<Name>Display Mode</Name>
|
||||
<DefaultValue>Scan</DefaultValue>
|
||||
<Value>Scan</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x33a30739, 0x00d5299b)</TypeIdentifier>
|
||||
<Name>Auto vertical scale</Name>
|
||||
<DefaultValue>Per channel</DefaultValue>
|
||||
<Value>Per channel</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Scale refresh interval (secs)</Name>
|
||||
<DefaultValue>5</DefaultValue>
|
||||
<Value>5</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Vertical Scale</Name>
|
||||
<DefaultValue>100</DefaultValue>
|
||||
<Value>100</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Vertical Offset</Name>
|
||||
<DefaultValue>0</DefaultValue>
|
||||
<Value>0</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Time Scale</Name>
|
||||
<DefaultValue>10</DefaultValue>
|
||||
<Value>10</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Horizontal ruler</Name>
|
||||
<DefaultValue>true</DefaultValue>
|
||||
<Value>true</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Vertical ruler</Name>
|
||||
<DefaultValue>false</DefaultValue>
|
||||
<Value>false</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Multiview</Name>
|
||||
<DefaultValue>false</DefaultValue>
|
||||
<Value>false</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>432.000000</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa963f5, 0x1a638cd4)</Identifier>
|
||||
<Value>51</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>272.000000</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x92c056a7, 0x2dc71aff)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x527ad68d, 0x16d746a0)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xad100179, 0xa3c984ab)</Identifier>
|
||||
<Value>88</Value>
|
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</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x00240e2e)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>9</Value>
|
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</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>3</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
</Boxes>
|
||||
<Links>
|
||||
<Link>
|
||||
<Identifier>(0x00000568, 0x00001560)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x000071e4, 0x00006ad6)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
||||
</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x0000721d, 0x00006b03)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
||||
</Target>
|
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|
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<Identifier>(0x1b32c44c, 0x1905e0e9)</Identifier>
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<Value>224</Value>
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<Identifier>(0x358ae8b5, 0x0f8bacd1)</Identifier>
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<Value>336</Value>
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</Attribute>
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|
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<Identifier>(0x3f0a3b27, 0x570913d2)</Identifier>
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<Value>402</Value>
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</Attribute>
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<Attribute>
|
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<Identifier>(0x6267b5c5, 0x676e3e42)</Identifier>
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<Value>257</Value>
|
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</Attribute>
|
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</Attributes>
|
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</Link>
|
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<Link>
|
||||
<Identifier>(0x00000730, 0x0000533a)</Identifier>
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<Source>
|
||||
<BoxIdentifier>(0x00005e30, 0x0000017a)</BoxIdentifier>
|
||||
<BoxOutputIndex>1</BoxOutputIndex>
|
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</Source>
|
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<Target>
|
||||
<BoxIdentifier>(0x000069dc, 0x00007101)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
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</Target>
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<Attributes>
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|
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<Identifier>(0x1b32c44c, 0x1905e0e9)</Identifier>
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<Value>51</Value>
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<Identifier>(0x358ae8b5, 0x0f8bacd1)</Identifier>
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<Value>400</Value>
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</Attribute>
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<Attribute>
|
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<Identifier>(0x3f0a3b27, 0x570913d2)</Identifier>
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<Value>79</Value>
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</Attribute>
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<Attribute>
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<Identifier>(0x6267b5c5, 0x676e3e42)</Identifier>
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<Value>400</Value>
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</Attribute>
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</Attributes>
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</Link>
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<Link>
|
||||
<Identifier>(0x0000150f, 0x00005c52)</Identifier>
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<Source>
|
||||
<BoxIdentifier>(0x000053ed, 0x0000612d)</BoxIdentifier>
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<BoxOutputIndex>0</BoxOutputIndex>
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</Source>
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<Target>
|
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<BoxIdentifier>(0x00004d05, 0x00007de5)</BoxIdentifier>
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<BoxInputIndex>0</BoxInputIndex>
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</Target>
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<Identifier>(0x1b32c44c, 0x1905e0e9)</Identifier>
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<Value>313</Value>
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<Value>416</Value>
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<Value>402</Value>
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<Identifier>(0x6267b5c5, 0x676e3e42)</Identifier>
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<Value>401</Value>
|
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</Attribute>
|
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</Attributes>
|
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</Link>
|
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<Link>
|
||||
<Identifier>(0x000018c1, 0x00002528)</Identifier>
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|
||||
<BoxIdentifier>(0x000069dc, 0x00007101)</BoxIdentifier>
|
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<BoxOutputIndex>0</BoxOutputIndex>
|
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</Source>
|
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<Target>
|
||||
<BoxIdentifier>(0x00003b19, 0x00002e65)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
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</Target>
|
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<Attributes>
|
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<Attribute>
|
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<Identifier>(0x1b32c44c, 0x1905e0e9)</Identifier>
|
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<Value>115</Value>
|
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</Attribute>
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|
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<Identifier>(0x358ae8b5, 0x0f8bacd1)</Identifier>
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<Value>400</Value>
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<Value>162</Value>
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|
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<Identifier>(0x6267b5c5, 0x676e3e42)</Identifier>
|
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<Value>464</Value>
|
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</Attribute>
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</Attributes>
|
||||
</Link>
|
||||
<Link>
|
||||
<Identifier>(0x00003155, 0x00006d63)</Identifier>
|
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|
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<BoxIdentifier>(0x00003b19, 0x00002e65)</BoxIdentifier>
|
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<BoxOutputIndex>0</BoxOutputIndex>
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</Source>
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|
||||
<BoxIdentifier>(0x000053ed, 0x0000612d)</BoxIdentifier>
|
||||
<BoxInputIndex>1</BoxInputIndex>
|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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<Value>423</Value>
|
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</Attribute>
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</Attributes>
|
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|
||||
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|
||||
<Identifier>(0x00003ea1, 0x0000322f)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x000069dc, 0x00007101)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
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</Source>
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<Target>
|
||||
<BoxIdentifier>(0x000071e4, 0x00006ad6)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
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<Identifier>(0x1b32c44c, 0x1905e0e9)</Identifier>
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|
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<Value>336</Value>
|
||||
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</Attributes>
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||||
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|
||||
<Link>
|
||||
<Identifier>(0x0000515e, 0x00004b1e)</Identifier>
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<Source>
|
||||
<BoxIdentifier>(0x000071e4, 0x00006ad6)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
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</Source>
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<Target>
|
||||
<BoxIdentifier>(0x000053ed, 0x0000612d)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
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</Target>
|
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<Attributes>
|
||||
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|
||||
<Identifier>(0x1b32c44c, 0x1905e0e9)</Identifier>
|
||||
<Value>224</Value>
|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0x358ae8b5, 0x0f8bacd1)</Identifier>
|
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<Value>336</Value>
|
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|
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|
||||
<Identifier>(0x3f0a3b27, 0x570913d2)</Identifier>
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<Value>264</Value>
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|
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|
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<Identifier>(0x6267b5c5, 0x676e3e42)</Identifier>
|
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<Value>408</Value>
|
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</Attribute>
|
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</Attributes>
|
||||
</Link>
|
||||
</Links>
|
||||
<Comments>
|
||||
<Comment>
|
||||
<Identifier>(0x00007d4d, 0x00006501)</Identifier>
|
||||
<Text>This scenario performs denoising following
|
||||
the work of Schlögl and al., 2007.
|
||||
|
||||
The model should be first calibrated using
|
||||
the 'eog-calibration.xml' scenario.
|
||||
|
||||
<b><i>EOG Denoising</i></b> box uses the estimated model
|
||||
and attempts to clean the EEG input of artifacts.
|
||||
</Text>
|
||||
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|
||||
<Attribute>
|
||||
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||||
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|
||||
<Attribute>
|
||||
<Identifier>(0x7234b86b, 0x2b8651a5)</Identifier>
|
||||
<Value>224.000000</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Comment>
|
||||
</Comments>
|
||||
<Metadata>
|
||||
<Entry>
|
||||
<Identifier>(0x0000775c, 0x000078ff)</Identifier>
|
||||
<Type>(0x3bcce5d2, 0x43f2d968)</Type>
|
||||
<Data>[{"boxIdentifier":"(0xffffffff, 0xffffffff)","childCount":1,"height":320,"identifier":"(0x00006444, 0x00003b19)","index":0,"name":"Default window","parentIdentifier":"(0xffffffff, 0xffffffff)","type":1,"width":467},{"boxIdentifier":"(0xffffffff, 0xffffffff)","childCount":1,"identifier":"(0x0000408e, 0x00005287)","index":0,"name":"Default tab","parentIdentifier":"(0x00006444, 0x00003b19)","type":2},{"boxIdentifier":"(0xffffffff, 0xffffffff)","childCount":2,"dividerPosition":135,"identifier":"(0x00004ee6, 0x00007589)","index":0,"maxDividerPosition":275,"name":"Vertical split","parentIdentifier":"(0x0000408e, 0x00005287)","type":4},{"boxIdentifier":"(0x0000721d, 0x00006b03)","childCount":0,"identifier":"(0x00004fca, 0x000005fa)","index":0,"parentIdentifier":"(0x00004ee6, 0x00007589)","type":3},{"boxIdentifier":"(0x00004d05, 0x00007de5)","childCount":0,"identifier":"(0x00005ad7, 0x00000e3a)","index":1,"parentIdentifier":"(0x00004ee6, 0x00007589)","type":3}]</Data>
|
||||
</Entry>
|
||||
</Metadata>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x790d75b8, 0x3bb90c33)</Identifier>
|
||||
<Value>Joao-Pedro Berti-Ligabo</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x8c1fc55b, 0x7b433dc2)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x9f5c4075, 0x4a0d3666)</Identifier>
|
||||
<Value>EOG: Calibration</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xf36a1567, 0xd13c53da)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xf6b2e3fa, 0x7bd43926)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xf8034a49, 0x8b3f37cc)</Identifier>
|
||||
<Value>Inria</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</OpenViBE-Scenario>
|
||||
+50
@@ -0,0 +1,50 @@
|
||||
|
||||
g_sent = false
|
||||
g_numTrials = 10
|
||||
|
||||
function initialize(box)
|
||||
dofile(box:get_config("${Path_Data}") .. "/plugins/stimulation/lua-stimulator-stim-codes.lua")
|
||||
-- make sure we get equal number of both trials
|
||||
g_numTrials = 2 * box:get_setting(2)
|
||||
g_sent = false;
|
||||
end
|
||||
|
||||
function uninitialize(box)
|
||||
end
|
||||
|
||||
function process(box)
|
||||
|
||||
while box:keep_processing() and g_sent == false do
|
||||
|
||||
box:send_stimulation(1, OVTK_StimulationId_ExperimentStart, 0, 0)
|
||||
|
||||
current_time = 5
|
||||
|
||||
for i = 1 , g_numTrials do
|
||||
|
||||
if i % 2 == 0 then
|
||||
box:send_stimulation(1, OVTK_GDF_Left, current_time+0, 0)
|
||||
else
|
||||
box:send_stimulation(1, OVTK_GDF_Right, current_time+0, 0)
|
||||
end
|
||||
box:send_stimulation(1, OVTK_StimulationId_Label_01, current_time+1, 0)
|
||||
box:send_stimulation(1, OVTK_StimulationId_Label_00, current_time+2, 0)
|
||||
box:send_stimulation(1, OVTK_StimulationId_Label_01, current_time+3, 0)
|
||||
box:send_stimulation(1, OVTK_StimulationId_Label_00, current_time+4, 0)
|
||||
box:send_stimulation(1, OVTK_StimulationId_RestStart, current_time+5, 0)
|
||||
|
||||
current_time = current_time + 10
|
||||
|
||||
end
|
||||
|
||||
box:send_stimulation(1, OVTK_StimulationId_ExperimentStop, current_time, 0)
|
||||
box:send_stimulation(1, OVTK_StimulationId_EndOfFile, current_time+2, 0)
|
||||
|
||||
g_sent = true
|
||||
|
||||
box:sleep()
|
||||
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
+2940
File diff suppressed because it is too large
Load Diff
+1011
File diff suppressed because it is too large
Load Diff
+2080
File diff suppressed because it is too large
Load Diff
+1011
File diff suppressed because it is too large
Load Diff
BIN
Binary file not shown.
|
After Width: | Height: | Size: 558 B |
BIN
Binary file not shown.
|
After Width: | Height: | Size: 718 B |
BIN
Binary file not shown.
|
After Width: | Height: | Size: 939 B |
+81
@@ -0,0 +1,81 @@
|
||||
/**
|
||||
* \page BoxAlgorithm_AutoRegressiveCoefficients AR Features
|
||||
__________________________________________________________________
|
||||
|
||||
Detailed description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_AutoRegressiveCoefficients_Description|
|
||||
*The AR features box calculate the coefficients using Burg's method [1] to compute the AutoRegressive (AR) model of an input signal.
|
||||
The AR model is a representation that describes a time varying process by its own previous values.
|
||||
|
||||
The definition used is :
|
||||
\image html ARBurg_Formula.png
|
||||
|
||||
<!-- "Formula of the AR model is x(t)=\sum_{i=1}^N a(i)x(t-i)+e(t)" -->
|
||||
|
||||
Where \e a(i) are the autoregressive coefficients or parameters of the model, \e x(t) is the input signal, \e x(t-i) its previous values, \e N is the order (length) of the model and \e epsilon(t) is the residue, assumed to be Gaussian white noise.
|
||||
|
||||
For more informations about AR model :
|
||||
|
||||
https://en.wikipedia.org/wiki/Autoregressive_model
|
||||
|
||||
http://paulbourke.net/miscellaneous/ar/
|
||||
|
||||
The model order (see [2]) needs to be specified in the settings of the box.
|
||||
|
||||
|
||||
[1] Burg, J.P. (1967) "Maximum Entropy Spectral Analysis", Proceedings of the 37th Meeting of the Society of Exploration Geophysicists, Oklahoma City, Oklahoma
|
||||
|
||||
[2] D.J. Krusienski, D.J. MacFarland, J.R. Wolpaw. An evaluation of autoregressive spectral estimation model order for brain-computer interface application. Proceedings of the 28th IEEE EMBS Annual International Conference, New York City, USA, Aug 30-Sept 3, 2006
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_AutoRegressiveCoefficients_Description|
|
||||
__________________________________________________________________
|
||||
|
||||
Inputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_AutoRegressiveCoefficients_Inputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_AutoRegressiveCoefficients_Inputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_AutoRegressiveCoefficients_Input1|
|
||||
The input signal
|
||||
* |OVP_DocEnd_BoxAlgorithm_AutoRegressiveCoefficients_Input1|
|
||||
__________________________________________________________________
|
||||
|
||||
Outputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_AutoRegressiveCoefficients_Outputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_AutoRegressiveCoefficients_Outputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_AutoRegressiveCoefficients_Output1|
|
||||
The AR coefficients stored in a Feature vector
|
||||
* |OVP_DocEnd_BoxAlgorithm_AutoRegressiveCoefficients_Output1|
|
||||
__________________________________________________________________
|
||||
|
||||
Settings description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_AutoRegressiveCoefficients_Settings|
|
||||
* |OVP_DocEnd_BoxAlgorithm_AutoRegressiveCoefficients_Settings|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_AutoRegressiveCoefficients_Setting1|
|
||||
Specify the order, thus the number of coefficients calculated
|
||||
* |OVP_DocEnd_BoxAlgorithm_AutoRegressiveCoefficients_Setting1|
|
||||
__________________________________________________________________
|
||||
|
||||
Examples description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_AutoRegressiveCoefficients_Examples|
|
||||
* |OVP_DocEnd_BoxAlgorithm_AutoRegressiveCoefficients_Examples|
|
||||
__________________________________________________________________
|
||||
|
||||
Miscellaneous description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_AutoRegressiveCoefficients_Miscellaneous|
|
||||
The output feature vector contains the coefficients for each channel : the first [order+1] elements are the coefficients of the first channel, etc.
|
||||
* |OVP_DocEnd_BoxAlgorithm_AutoRegressiveCoefficients_Miscellaneous|
|
||||
*/
|
||||
+93
@@ -0,0 +1,93 @@
|
||||
/**
|
||||
* \page BoxAlgorithm_ConnectivityMeasure Connectivity Measure
|
||||
__________________________________________________________________
|
||||
|
||||
Detailed description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ConnectivityMeasure_Description|
|
||||
This box measure connectivity between all channels of a signal using several method. For now, Coherence, Magnitude Squared Coherence, Imaginary part of Coherence, and absolute value of the Imaginary part are available.
|
||||
They are defined in [1].
|
||||
|
||||
The coherence definitions used are :
|
||||
\f[ Coherence = \frac{\left| S_{xy} \right|}{sqrt{(P_{xx}.P_{yy})} } \f]
|
||||
\f[ Magnitude Squared Coherence = \frac{\left| S_{xy} \right|^2}{(P_{xx}.P_{yy})} \f]
|
||||
\f[ Imaginary Coherence = \frac{Im(S_{xy})}{sqrt{(P_{xx}.P_{yy})} } \f]
|
||||
\f[ Absolute Value Of Imaginary Coherence = \frac{\left| Im(S_{xy}) \right|}{sqrt{(P_{xx}.P_{yy})} } \f]
|
||||
|
||||
With \e \f$ S_{xy} \f$ the cross-spectral density between two signal channels, and \e \f$ P_{xx} \f$ and \e \f$ P_{yy} \f$ the Power Spectral Densities of the two channels.
|
||||
The spectral densities are estimated via Welch's method [2]
|
||||
|
||||
[1] Nolte & al (2004) "Identifying true brain interaction from EEG data using the imaginary part of coherency", Clinical Neurophysiology Volume 115, Issue 10, October 2004
|
||||
|
||||
[2] Welch, P.D. (1967) "The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms", IEEE Transactions on Audio Electroacoustics, AU-15, 70–73
|
||||
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_ConnectivityMeasure_Description|
|
||||
__________________________________________________________________
|
||||
|
||||
Inputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ConnectivityMeasure_Inputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_ConnectivityMeasure_Inputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ConnectivityMeasure_Input1|
|
||||
The input signal on which connectivity between channels will be measured.
|
||||
* |OVP_DocEnd_BoxAlgorithm_ConnectivityMeasure_Input1|
|
||||
__________________________________________________________________
|
||||
|
||||
Outputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ConnectivityMeasure_Outputs|
|
||||
The output of the box is the connectivity Matrix
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_ConnectivityMeasure_Outputs|
|
||||
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ConnectivityMeasure_Output1|
|
||||
The connectivity matrix is a 3D Matrix of size \e frequency_taps \e x \e nb_channels \e x \e nb_channels
|
||||
* |OVP_DocEnd_BoxAlgorithm_ConnectivityMeasure_Output1|
|
||||
|
||||
|
||||
|
||||
__________________________________________________________________
|
||||
|
||||
Settings description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ConnectivityMeasure_Settings|
|
||||
* |OVP_DocEnd_BoxAlgorithm_ConnectivityMeasure_Settings|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ConnectivityMeasure_Setting1|
|
||||
Choice of the algorithm to measure the connectivity (Magnitude Squared Coherence, Imaginary Coherence).
|
||||
* |OVP_DocEnd_BoxAlgorithm_ConnectivityMeasure_Setting1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ConnectivityMeasure_Setting2|
|
||||
The windowing method to apply. Available options are Hamming, Hanning and Welch.
|
||||
* |OVP_DocEnd_BoxAlgorithm_ConnectivityMeasure_Setting2|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ConnectivityMeasure_Setting3|
|
||||
The length of the window in seconds for the windowing method.
|
||||
* |OVP_DocEnd_BoxAlgorithm_ConnectivityMeasure_Setting3|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ConnectivityMeasure_Setting4|
|
||||
The percentage of overlap of the windowing method
|
||||
* |OVP_DocEnd_BoxAlgorithm_ConnectivityMeasure_Setting4|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ConnectivityMeasure_Setting5|
|
||||
The length of signal to receive (in seconds) before processing the connectivity on it.
|
||||
* |OVP_DocEnd_BoxAlgorithm_ConnectivityMeasure_Setting5|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ConnectivityMeasure_Setting6|
|
||||
The percentage of signal overlap to process the connectivity on.
|
||||
* |OVP_DocEnd_BoxAlgorithm_ConnectivityMeasure_Setting6|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ConnectivityMeasure_Setting7|
|
||||
The amount of frequency taps for the connectivity measure.
|
||||
* |OVP_DocEnd_BoxAlgorithm_ConnectivityMeasure_Setting7|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ConnectivityMeasure_Setting8|
|
||||
Option to remove DC component from signal.
|
||||
* |OVP_DocEnd_BoxAlgorithm_ConnectivityMeasure_Setting8|
|
||||
BIN
Binary file not shown.
|
After Width: | Height: | Size: 2.8 KiB |
+79
@@ -0,0 +1,79 @@
|
||||
/**
|
||||
* \page BoxAlgorithm_DiscreteWaveletTransform DiscreteWaveletTransform
|
||||
__________________________________________________________________
|
||||
|
||||
Detailed description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_DiscreteWaveletTransform_Description|
|
||||
* This box calculates the discrete wavelet transform using the following library:
|
||||
http://wavelet2d.sourceforge.net/
|
||||
There are differents options for the wavelet's choice like Haar,Daubechie, Biorthogonal, Coiflets, Symlets.
|
||||
The user must pay attention in the samples quantity sent to the box because it has to be higher than 2^J
|
||||
where J is the decomposition levels.
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_DiscreteWaveletTransform_Description|
|
||||
__________________________________________________________________
|
||||
|
||||
Inputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_DiscreteWaveletTransform_Inputs|
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_DiscreteWaveletTransform_Inputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_DiscreteWaveletTransform_Input1|
|
||||
Signal to be decomposed.
|
||||
* |OVP_DocEnd_BoxAlgorithm_DiscreteWaveletTransform_Input1|
|
||||
__________________________________________________________________
|
||||
|
||||
Outputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_DiscreteWaveletTransform_Outputs|
|
||||
There are at least 3 outputs and there is always the 'Info' output which is necessary to be connected to the
|
||||
'Info' signal in the inverse discrete wavelets transform.
|
||||
The decompositions start with the less detailed level (low frequencies) (A) and go until the highest detailed level (high frequencies) (D1)
|
||||
* |OVP_DocEnd_BoxAlgorithm_DiscreteWaveletTransform_Outputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_DiscreteWaveletTransform_Output1|
|
||||
Info signal (needed to export samples length)
|
||||
* |OVP_DocEnd_BoxAlgorithm_DiscreteWaveletTransform_Output1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_DiscreteWaveletTransform_Output2|
|
||||
* |OVP_DocEnd_BoxAlgorithm_DiscreteWaveletTransform_Output2|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_DiscreteWaveletTransform_Output3|
|
||||
* |OVP_DocEnd_BoxAlgorithm_DiscreteWaveletTransform_Output3|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_DiscreteWaveletTransform_Output4|
|
||||
* |OVP_DocEnd_BoxAlgorithm_DiscreteWaveletTransform_Output4|
|
||||
__________________________________________________________________
|
||||
|
||||
Settings description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_DiscreteWaveletTransform_Settings|
|
||||
You can choose the level of decomposition and the wavelet type
|
||||
* |OVP_DocEnd_BoxAlgorithm_DiscreteWaveletTransform_Settings|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_DiscreteWaveletTransform_Setting1|
|
||||
* |OVP_DocEnd_BoxAlgorithm_DiscreteWaveletTransform_Setting1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_DiscreteWaveletTransform_Setting2|
|
||||
* |OVP_DocEnd_BoxAlgorithm_DiscreteWaveletTransform_Setting2|
|
||||
__________________________________________________________________
|
||||
|
||||
Examples description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_DiscreteWaveletTransform_Examples|
|
||||
* |OVP_DocEnd_BoxAlgorithm_DiscreteWaveletTransform_Examples|
|
||||
__________________________________________________________________
|
||||
|
||||
Miscellaneous description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_DiscreteWaveletTransform_Miscellaneous|
|
||||
* |OVP_DocEnd_BoxAlgorithm_DiscreteWaveletTransform_Miscellaneous|
|
||||
*/
|
||||
+69
@@ -0,0 +1,69 @@
|
||||
/**
|
||||
* \page BoxAlgorithm_EOGDenoising EOG Denoising
|
||||
__________________________________________________________________
|
||||
|
||||
Detailed description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoising_Description|
|
||||
* This box uses a denoising matrix 'b' calculated previously through the EOG_Denoising_Calibration for removing the EOG effects on EEG. The principle is based on regression analysis (see article 'A fully automated correction method of EOG artifacts in EEG recordings) where a matrix 'b' is estimated being:b = <'Nt N>-¹<'N S> with N being the noise (EOG electrodes) and S the source (EEG electrodes).
|
||||
The signal output is the EEG_Corrected (free of EOG noise):O=S-b*N (EEG_Corrected = EEG - b*EOG)
|
||||
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoising_Description|
|
||||
__________________________________________________________________
|
||||
|
||||
Inputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoising_Inputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoising_Inputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoising_Input1|
|
||||
* Make sure to select the same quantity of EEG channels as specified in your 'b' parameter matrix
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoising_Input1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoising_Input2|
|
||||
* Make sure to select the same quantity of EOG channels as specified in your 'b' parameter matrix
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoising_Input2|
|
||||
__________________________________________________________________
|
||||
|
||||
Outputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoising_Outputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoising_Outputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoising_Output1|
|
||||
* The output has the same structure as the EEG input
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoising_Output1|
|
||||
__________________________________________________________________
|
||||
|
||||
Settings description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoising_Settings|
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoising_Settings|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoising_Setting1|
|
||||
* Make sure to select the right file containing your 'b' matrix with the right coefficients
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoising_Setting1|
|
||||
__________________________________________________________________
|
||||
|
||||
Examples description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoising_Examples|
|
||||
* You can apply these boxes in a set with a high density of blinking eyes and see the results before and after.
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoising_Examples|
|
||||
__________________________________________________________________
|
||||
|
||||
Miscellaneous description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoising_Miscellaneous|
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoising_Miscellaneous|
|
||||
*/
|
||||
+77
@@ -0,0 +1,77 @@
|
||||
/**
|
||||
* \page BoxAlgorithm_EOGDenoisingCalibration EOG_Denoising_Calibration
|
||||
__________________________________________________________________
|
||||
|
||||
Detailed description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoisingCalibration_Description|
|
||||
This box calculates a denoising matrix 'b' for removing the EOG effects on EEG. The principle is based on regression analysis (see article 'A fully automated correction method of EOG artifacts in EEG recordings' - Schlogl2007) where a matrix 'b' is estimated being: b = <'Nt N>-¹<'N S> with N being the noise (EOG electrodes) and S the source (EEG electrodes).
|
||||
This box reads necessarily from a file where the subject does lots of blinks. User will set a starting and an ending point at this file then the program will calculates the b matrix after reading throughout the file.
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoisingCalibration_Description|
|
||||
__________________________________________________________________
|
||||
|
||||
Inputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoisingCalibration_Inputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoisingCalibration_Inputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoisingCalibration_Input1|
|
||||
* Make sure to select the desirable EEG channels
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoisingCalibration_Input1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoisingCalibration_Input2|
|
||||
* Make sure to select the desirable EOG channels
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoisingCalibration_Input2|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoisingCalibration_Input3|
|
||||
* Connect this stimulation to the keyboard_controller.
|
||||
* Press 'a' for set the starting point and 'u' for set the ending point
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoisingCalibration_Input3|
|
||||
__________________________________________________________________
|
||||
|
||||
Outputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoisingCalibration_Outputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoisingCalibration_Outputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoisingCalibration_Output1|
|
||||
Connect this stimulation to the Player Controller to stop the scenario when b matrix is calculated
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoisingCalibration_Output1|
|
||||
__________________________________________________________________
|
||||
|
||||
Settings description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoisingCalibration_Settings|
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoisingCalibration_Settings|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoisingCalibration_Setting1|
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoisingCalibration_Setting1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoisingCalibration_Setting2|
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoisingCalibration_Setting2|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoisingCalibration_Setting3|
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoisingCalibration_Setting3|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoisingCalibration_Setting4|
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoisingCalibration_Setting4|
|
||||
__________________________________________________________________
|
||||
|
||||
Examples description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoisingCalibration_Examples|
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoisingCalibration_Examples|
|
||||
__________________________________________________________________
|
||||
|
||||
Miscellaneous description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EOGDenoisingCalibration_Miscellaneous|
|
||||
* |OVP_DocEnd_BoxAlgorithm_EOGDenoisingCalibration_Miscellaneous|
|
||||
*/
|
||||
+96
@@ -0,0 +1,96 @@
|
||||
/**
|
||||
* \page BoxAlgorithm_ERSPAverage ERSP Average
|
||||
__________________________________________________________________
|
||||
|
||||
Detailed description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ERSPAverage_Description|
|
||||
|
||||
The ERSP Average box is intended to be used for computing
|
||||
Event-Related Spectral Perturbation (ERSP) plots. These plots
|
||||
show, starting from a stimulus onset, how the power spectrum
|
||||
develops over time on the average across the trials. This is not
|
||||
straightforwardly achievable with the other averaging boxes,
|
||||
as the OpenViBE Spectrum stream does not have a time dimension:
|
||||
a spectrum chunk is a matrix [frequency X channel], whereas to compute
|
||||
the average evolution of a spectra over time, we would need a
|
||||
tensor [frequency X channel X time] per trial and then average
|
||||
these across the trials.
|
||||
|
||||
To achieve the average, the ERSP Average box collects individual
|
||||
spectra, and computes the average when requested. In more detail,
|
||||
assume you have EEG data for trial t. Using other boxes, you can
|
||||
segment this trial to k spectral power estimates s1,s2,...,sk.
|
||||
Now what the ERSP average does is to average these estimates
|
||||
across the trials, and returns E[s1], E[s2], ..., E[sk], which
|
||||
you can then plot as the average evolution of the spectrum
|
||||
after the stimulus onset.
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_ERSPAverage_Description|
|
||||
__________________________________________________________________
|
||||
|
||||
Inputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ERSPAverage_Inputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_ERSPAverage_Inputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ERSPAverage_Input1|
|
||||
|
||||
The spectrum stream to average
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_ERSPAverage_Input1|
|
||||
__________________________________________________________________
|
||||
|
||||
Outputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ERSPAverage_Outputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_ERSPAverage_Outputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ERSPAverage_Output1|
|
||||
|
||||
A sequence of chunks encoding the evolution of the spectra after stimulus onset.
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_ERSPAverage_Output1|
|
||||
__________________________________________________________________
|
||||
|
||||
Settings description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ERSPAverage_Settings|
|
||||
* |OVP_DocEnd_BoxAlgorithm_ERSPAverage_Settings|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ERSPAverage_Setting1|
|
||||
|
||||
The stimulation to identify the trial start (stimulus onset).
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_ERSPAverage_Setting1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ERSPAverage_Setting2|
|
||||
|
||||
The stimulation to trigger the computation of the average.
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_ERSPAverage_Setting2|
|
||||
__________________________________________________________________
|
||||
|
||||
Examples description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ERSPAverage_Examples|
|
||||
|
||||
See the box tutorial ersp-average.mxs bundled with OpenViBE.
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_ERSPAverage_Examples|
|
||||
__________________________________________________________________
|
||||
|
||||
Miscellaneous description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ERSPAverage_Miscellaneous|
|
||||
|
||||
The box does not return anything until the computation trigger is received. After that, its output is set to start from time 0. Upon computation, its buffers and counters will be automatically cleared.
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_ERSPAverage_Miscellaneous|
|
||||
*/
|
||||
+186
@@ -0,0 +1,186 @@
|
||||
/**
|
||||
* \page BoxAlgorithm_EpochVariance Epoch variance
|
||||
__________________________________________________________________
|
||||
|
||||
Detailed description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EpochVariance_Description|
|
||||
* This box is an extension of the Epoch Average box. It offers several methods of averaging for epoched streams but also outputs variance and confidence bounds.
|
||||
* |OVP_DocEnd_BoxAlgorithm_EpochVariance_Description|
|
||||
__________________________________________________________________
|
||||
|
||||
Inputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EpochVariance_Inputs|
|
||||
* The input type of this box can be changed. Its type must be derived of
|
||||
* type \ref Doc_Streams_StreamedMatrix in order to be parsed by the input
|
||||
* reader. If the author changes the input type, the output type will
|
||||
* be changed the same way.
|
||||
* |OVP_DocEnd_BoxAlgorithm_EpochVariance_Inputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EpochVariance_Input1|
|
||||
* This input receives the input streamed matrix to average.
|
||||
* |OVP_DocEnd_BoxAlgorithm_EpochVariance_Input1|
|
||||
__________________________________________________________________
|
||||
|
||||
Outputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EpochVariance_Outputs|
|
||||
* The output type of this box can be changed. Its type must be derived of
|
||||
* type \ref Doc_Streams_StreamedMatrix in order for the writer to format
|
||||
* the output chunks. If the author changes the output type, the input
|
||||
* type will be changed the same way.
|
||||
* |OVP_DocEnd_BoxAlgorithm_EpochVariance_Outputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EpochVariance_Output1|
|
||||
* This output sends the averaged streamed matrix. Averaging method is done
|
||||
* according to the box settings.
|
||||
* |OVP_DocEnd_BoxAlgorithm_EpochVariance_Output1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EpochVariance_Output2|
|
||||
* This output sends the variance of the input. Averaging method is done
|
||||
* according to the box settings.
|
||||
* |OVP_DocEnd_BoxAlgorithm_EpochVariance_Output2|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EpochVariance_Output3|
|
||||
* This output sends the confidence bounds of the input. Averaging method is done
|
||||
* according to the box settings.
|
||||
* |OVP_DocEnd_BoxAlgorithm_EpochVariance_Output3|
|
||||
__________________________________________________________________
|
||||
|
||||
Settings description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EpochVariance_Settings|
|
||||
* |OVP_DocEnd_BoxAlgorithm_EpochVariance_Settings|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EpochVariance_Setting1|
|
||||
* This setting gives the method to use in order to average the input
|
||||
* matrices. It can be of two types :
|
||||
* - <em>Moving average</em> : in this case, the averaging is done at
|
||||
* every input reception on the last few buffers, starting as soon
|
||||
* as enough input has been received.
|
||||
* - <em>Moving average (Immediate)</em> : in this case, the averaging is done at
|
||||
* every input reception on the last few buffers, starting immediately. When
|
||||
* the number of received buffer is lower than the wished number of epochs, the
|
||||
* average is computed on this very few number of input buffers.
|
||||
* - <em>Epoch block average</em> : in this case, the averaging
|
||||
* is done on a number of epochs (see next setting). Once this exact
|
||||
* number of input is received, the average is computed and output.
|
||||
* - <em>Cumulative average</em> : in this case, the averaging
|
||||
* is done on an infinite number of epochs starting from the first
|
||||
* received buffer to the last received buffer. This can be \b very
|
||||
* memory consuming !
|
||||
* |OVP_DocEnd_BoxAlgorithm_EpochVariance_Setting1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EpochVariance_Setting2|
|
||||
* This setting tells the box how much buffer it should use in order to
|
||||
* compute the average.
|
||||
* |OVP_DocEnd_BoxAlgorithm_EpochVariance_Setting2|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EpochVariance_Setting3|
|
||||
* Significance Level for the confidence bound computation.
|
||||
* The higher it is, the tighter the confidence interval will be.
|
||||
* |OVP_DocEnd_BoxAlgorithm_EpochVariance_Setting3|
|
||||
__________________________________________________________________
|
||||
|
||||
Examples description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EpochVariance_Examples|
|
||||
* Let's study two cases. First, suppose you have such box with
|
||||
* <em>Epoch block average</em> set and <em>four</em> epochs.
|
||||
* The input stream is as follows :
|
||||
\verbatim
|
||||
+----+ +----+ +----+ +----+ +----+ +----+ +----+ +----+ +----+
|
||||
| I1 | | I2 | | I3 | | I4 | | I5 | | I6 | | I7 | | I8 | | I9 | ...
|
||||
+----+ +----+ +----+ +----+ +----+ +----+ +----+ +----+ +----+
|
||||
\endverbatim
|
||||
* The output stream will look like this :
|
||||
\verbatim
|
||||
+----+ +----+
|
||||
| O1 | | O2 | ...
|
||||
+----+ +----+
|
||||
\endverbatim
|
||||
* where \c O1 is the average of \c I1, \c I2, \c I3 and \c I4 and
|
||||
* where \c O2 is the average of \c I5, \c I6, \c I7 and \c I8.
|
||||
*
|
||||
* Now consider the case where you configured this box with
|
||||
* <em>Moving average</em> and <em>four</em> epochs. Given the
|
||||
* same input stream :
|
||||
\verbatim
|
||||
+----+ +----+ +----+ +----+ +----+ +----+ +----+ +----+ +----+
|
||||
| I1 | | I2 | | I3 | | I4 | | I5 | | I6 | | I7 | | I8 | | I9 | ...
|
||||
+----+ +----+ +----+ +----+ +----+ +----+ +----+ +----+ +----+
|
||||
\endverbatim
|
||||
* The output stream will look like this :
|
||||
\verbatim
|
||||
+----+ +----+ +----+ +----+ +----+ +----+
|
||||
| O1 | | O2 | | O3 | | O4 | | O5 | | O6 | ...
|
||||
+----+ +----+ +----+ +----+ +----+ +----+
|
||||
\endverbatim
|
||||
* where :
|
||||
* - \c O1 is the average of \c I1, \c I2, \c I3 and \c I4
|
||||
* - \c O2 is the average of \c I2, \c I3, \c I4 and \c I5
|
||||
* - \c O3 is the average of \c I3, \c I4, \c I5 and \c I6
|
||||
* - \c O4 is the average of \c I4, \c I5, \c I6 and \c I7
|
||||
* - etc...
|
||||
*
|
||||
* Again consider the case where you configured this box with
|
||||
* <em>Moving average (Immediate)</em> and <em>four</em> epochs. Given the
|
||||
* same input stream :
|
||||
\verbatim
|
||||
+----+ +----+ +----+ +----+ +----+ +----+ +----+ +----+ +----+
|
||||
| I1 | | I2 | | I3 | | I4 | | I5 | | I6 | | I7 | | I8 | | I9 | ...
|
||||
+----+ +----+ +----+ +----+ +----+ +----+ +----+ +----+ +----+
|
||||
\endverbatim
|
||||
* The output stream will look like this :
|
||||
\verbatim
|
||||
+----+ +----+ +----+ +----+ +----+ +----+
|
||||
| O1 | | O2 | | O3 | | O4 | | O5 | | O6 | ...
|
||||
+----+ +----+ +----+ +----+ +----+ +----+
|
||||
\endverbatim
|
||||
* where :
|
||||
* - \c O1 is exactly \c I1
|
||||
* - \c O2 is the average of \c I1 and \c I2
|
||||
* - \c O3 is the average of \c I1, \c I2 and \c I3
|
||||
* - \c O4 is the average of \c I1, \c I2, \c I3 and \c I4
|
||||
* - \c O5 is the average of \c I2, \c I3, \c I4 and \c I5
|
||||
* - \c O6 is the average of \c I3, \c I4, \c I5 and \c I6
|
||||
* - etc...
|
||||
*
|
||||
* Finally consider the case where you configured this box with
|
||||
* <em>Cumulative average</em> and <em>four</em> epochs. Given the
|
||||
* same input stream :
|
||||
\verbatim
|
||||
+----+ +----+ +----+ +----+ +----+ +----+ +----+ +----+ +----+
|
||||
| I1 | | I2 | | I3 | | I4 | | I5 | | I6 | | I7 | | I8 | | I9 | ...
|
||||
+----+ +----+ +----+ +----+ +----+ +----+ +----+ +----+ +----+
|
||||
\endverbatim
|
||||
* The output stream will look like this :
|
||||
\verbatim
|
||||
+----+ +----+ +----+ +----+ +----+ +----+
|
||||
| O1 | | O2 | | O3 | | O4 | | O5 | | O6 | ...
|
||||
+----+ +----+ +----+ +----+ +----+ +----+
|
||||
\endverbatim
|
||||
* where :
|
||||
* - \c O1 is exactly \c I1
|
||||
* - \c O2 is the average of \c I1 and \c I2
|
||||
* - \c O3 is the average of \c I1, \c I2 and \c I3
|
||||
* - \c O4 is the average of \c I1, \c I2, \c I3 and \c I4
|
||||
* - \c O5 is the average of \c I1, \c I2, \c I3, \c I4, and \c I5
|
||||
* - \c O6 is the average of \c I1, \c I2, \c I3, \c I4, \c I5, and \c I6
|
||||
* - etc...
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_EpochVariance_Examples|
|
||||
__________________________________________________________________
|
||||
|
||||
Miscellaneous description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_EpochVariance_Miscellaneous|
|
||||
* |OVP_DocEnd_BoxAlgorithm_EpochVariance_Miscellaneous|
|
||||
*/
|
||||
+71
@@ -0,0 +1,71 @@
|
||||
/**
|
||||
* \page BoxAlgorithm_HilbertTransform Hilbert Transform
|
||||
__________________________________________________________________
|
||||
|
||||
Detailed description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_HilbertTransform_Description|
|
||||
|
||||
This box computes the envelope and the instantaneous phase by performing the Discrete-Time Analytic signal [1] using Hilbert transform (see http://en.wikipedia.org/wiki/Analytic_signal).
|
||||
|
||||
The definition of analytic signal :
|
||||
\image html AnalyticRepresentation.png
|
||||
|
||||
<!-- "Formula of the Analytic signal is x_a(t)=x(t)+i*H(x)(t)"-->
|
||||
|
||||
With \e x(t) the input signal, \e H(x) its Hilbert transform and \e i the imaginary unit.
|
||||
|
||||
For more informations on Hilbert transform and EEG see also : http://www.scholarpedia.org/article/Hilbert_transform_for_brain_waves
|
||||
|
||||
|
||||
[1] Marple, S.L., "Computing the discrete-time analytic signal via FFT," IEEE Transactions on Signal Processing, Vol. 47, No.9 (September 1999), pp.2600-2603.
|
||||
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_HilbertTransform_Description|
|
||||
__________________________________________________________________
|
||||
|
||||
Inputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_HilbertTransform_Inputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_HilbertTransform_Inputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_HilbertTransform_Input1|
|
||||
The input signal
|
||||
* |OVP_DocEnd_BoxAlgorithm_HilbertTransform_Input1|
|
||||
__________________________________________________________________
|
||||
|
||||
Outputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_HilbertTransform_Outputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_HilbertTransform_Outputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_HilbertTransform_Output1|
|
||||
Return the Hilbert transform (imaginary part of the analytic signal) of the input
|
||||
* |OVP_DocEnd_BoxAlgorithm_HilbertTransform_Output1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_HilbertTransform_Output2|
|
||||
Return the envelope signal of the input
|
||||
* |OVP_DocEnd_BoxAlgorithm_HilbertTransform_Output2|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_HilbertTransform_Output3|
|
||||
Return instantaneous phase of the input
|
||||
* |OVP_DocEnd_BoxAlgorithm_HilbertTransform_Output3|
|
||||
__________________________________________________________________
|
||||
|
||||
Examples description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_HilbertTransform_Examples|
|
||||
<!--TO DO : Add some examples and screenshots of the results-->
|
||||
* |OVP_DocEnd_BoxAlgorithm_HilbertTransform_Examples|
|
||||
__________________________________________________________________
|
||||
|
||||
Miscellaneous description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_HilbertTransform_Miscellaneous|
|
||||
* |OVP_DocEnd_BoxAlgorithm_HilbertTransform_Miscellaneous|
|
||||
*/
|
||||
+67
@@ -0,0 +1,67 @@
|
||||
/**
|
||||
* \page BoxAlgorithm_InverseDWT Inverse DWT
|
||||
__________________________________________________________________
|
||||
|
||||
Detailed description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_InverseDWT_Description|
|
||||
See Discrete Wavelet Transform box for more informations
|
||||
* |OVP_DocEnd_BoxAlgorithm_InverseDWT_Description|
|
||||
__________________________________________________________________
|
||||
|
||||
Inputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_InverseDWT_Inputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_InverseDWT_Inputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_InverseDWT_Input1|
|
||||
* |OVP_DocEnd_BoxAlgorithm_InverseDWT_Input1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_InverseDWT_Input2|
|
||||
* |OVP_DocEnd_BoxAlgorithm_InverseDWT_Input2|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_InverseDWT_Input3|
|
||||
* |OVP_DocEnd_BoxAlgorithm_InverseDWT_Input3|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_InverseDWT_Input4|
|
||||
* |OVP_DocEnd_BoxAlgorithm_InverseDWT_Input4|
|
||||
__________________________________________________________________
|
||||
|
||||
Outputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_InverseDWT_Outputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_InverseDWT_Outputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_InverseDWT_Output1|
|
||||
* |OVP_DocEnd_BoxAlgorithm_InverseDWT_Output1|
|
||||
__________________________________________________________________
|
||||
|
||||
Settings description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_InverseDWT_Settings|
|
||||
* |OVP_DocEnd_BoxAlgorithm_InverseDWT_Settings|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_InverseDWT_Setting1|
|
||||
* |OVP_DocEnd_BoxAlgorithm_InverseDWT_Setting1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_InverseDWT_Setting2|
|
||||
* |OVP_DocEnd_BoxAlgorithm_InverseDWT_Setting2|
|
||||
__________________________________________________________________
|
||||
|
||||
Examples description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_InverseDWT_Examples|
|
||||
* |OVP_DocEnd_BoxAlgorithm_InverseDWT_Examples|
|
||||
__________________________________________________________________
|
||||
|
||||
Miscellaneous description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_InverseDWT_Miscellaneous|
|
||||
* |OVP_DocEnd_BoxAlgorithm_InverseDWT_Miscellaneous|
|
||||
*/
|
||||
+51
@@ -0,0 +1,51 @@
|
||||
/**
|
||||
* \page BoxAlgorithm_Matrix3DTo2D 3D to 2D Matrix conversion
|
||||
__________________________________________________________________
|
||||
|
||||
Detailed description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_Matrix3DTo2D_Description|
|
||||
This box extracts a 2D matrix from a 3D matrix by removing a dimension and selecting a 2D matrix at the desired index
|
||||
of the removed dimension.
|
||||
* |OVP_DocEnd_BoxAlgorithm_Matrix3DTo2D_Description|
|
||||
__________________________________________________________________
|
||||
|
||||
Inputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_Matrix3DTo2D_Inputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_Matrix3DTo2D_Inputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_Matrix3DTo2D_Input1|
|
||||
3D Matrix from which a 2D matrix ( a "slice") will be extracted.
|
||||
* |OVP_DocEnd_BoxAlgorithm_Matrix3DTo2D_Input1|
|
||||
__________________________________________________________________
|
||||
|
||||
Outputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_Matrix3DTo2D_Outputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_Matrix3DTo2D_Outputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_Matrix3DTo2D_Output1|
|
||||
The 2D matrix extrated from the input Matrix
|
||||
* |OVP_DocEnd_BoxAlgorithm_Matrix3DTo2D_Output1|
|
||||
__________________________________________________________________
|
||||
|
||||
Settings description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_Matrix3DTo2D_Settings|
|
||||
* |OVP_DocEnd_BoxAlgorithm_Matrix3DTo2D_Settings|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_Matrix3DTo2D_Setting1|
|
||||
The dimension to remove from the 3D Matrix. Possible values are ranging in [ 0 - 2 ].
|
||||
* |OVP_DocEnd_BoxAlgorithm_Matrix3DTo2D_Setting1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_Matrix3DTo2D_Setting2|
|
||||
The index from the removed dimension at which to extract the 2 Matrix (the "slice").
|
||||
* |OVP_DocEnd_BoxAlgorithm_Matrix3DTo2D_Setting2|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_Matrix3DTo2D_Setting3|
|
||||
* |OVP_DocEnd_BoxAlgorithm_Matrix3DTo2D_Setting3|
|
||||
+88
@@ -0,0 +1,88 @@
|
||||
/**
|
||||
* \page BoxAlgorithm_QuadraticForm Quadratic Form
|
||||
__________________________________________________________________
|
||||
|
||||
Detailed description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_QuadraticForm_Description|
|
||||
|
||||
a square matrix A (which can be seen as a spatial filter) is applied to the input signals m (a vector). Then the transpose m^T of the input signals is multiplied to the resulting vector. In other words the output o is such as: o = m^T * A * m.
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_QuadraticForm_Description|
|
||||
__________________________________________________________________
|
||||
|
||||
Inputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_QuadraticForm_Inputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_QuadraticForm_Inputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_QuadraticForm_Input1|
|
||||
|
||||
The input signal to be used in the computation of the quadratic form
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_QuadraticForm_Input1|
|
||||
__________________________________________________________________
|
||||
|
||||
Outputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_QuadraticForm_Outputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_QuadraticForm_Outputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_QuadraticForm_Output1|
|
||||
|
||||
the results of the computation of the quadratic form, perform with the input signals and the matrix defined in the settings
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_QuadraticForm_Output1|
|
||||
__________________________________________________________________
|
||||
|
||||
Settings description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_QuadraticForm_Settings|
|
||||
* |OVP_DocEnd_BoxAlgorithm_QuadraticForm_Settings|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_QuadraticForm_Setting1|
|
||||
|
||||
The values of the matrix coefficients. These values are entered as a single line of values,
|
||||
which line should correspond to the concatenation of each matrix row.
|
||||
For instance the setting "1 2 3 4" corresponds to the matrix:
|
||||
|
||||
[1 2]
|
||||
[3 4]
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_QuadraticForm_Setting1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_QuadraticForm_Setting2|
|
||||
|
||||
The number of rows/columns of the matrix (the number of rows is equal to the number of columns as the matrix is square.
|
||||
For the matrix given as example above, this setting should be equal to "2".
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_QuadraticForm_Setting2|
|
||||
__________________________________________________________________
|
||||
|
||||
Examples description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_QuadraticForm_Examples|
|
||||
* |OVP_DocEnd_BoxAlgorithm_QuadraticForm_Examples|
|
||||
__________________________________________________________________
|
||||
|
||||
Miscellaneous description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_QuadraticForm_Miscellaneous|
|
||||
|
||||
this box could typically be used to compute the current density in a given brain region,
|
||||
for instance using the inverse solution sLORETA. In such a case the matrix using as parameter should be a
|
||||
matrix obtained thanks to this sLORETA inverse solution.
|
||||
see the following paper for details:
|
||||
|
||||
Congedo M. (2006), Subspace Projection Filters for Real-Time Brain Electromagnetic Imaging, IEEE Transactions on Biomedical Engineering, 53(8), 1624-34
|
||||
|
||||
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_QuadraticForm_Miscellaneous|
|
||||
*/
|
||||
+72
@@ -0,0 +1,72 @@
|
||||
/**
|
||||
* \page BoxAlgorithm_SignalDifferential_Integral Signal Differential/Integral
|
||||
__________________________________________________________________
|
||||
|
||||
Detailed description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SignalDifferential_Integral_Description|
|
||||
|
||||
This box can be used to calculate signal differential or integral of any order.
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_SignalDifferential_Integral_Description|
|
||||
__________________________________________________________________
|
||||
|
||||
Inputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SignalDifferential_Integral_Inputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_SignalDifferential_Integral_Inputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SignalDifferential_Integral_Input1|
|
||||
|
||||
The signal to be derivated/integrated
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_SignalDifferential_Integral_Input1|
|
||||
__________________________________________________________________
|
||||
|
||||
Outputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SignalDifferential_Integral_Outputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_SignalDifferential_Integral_Outputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SignalDifferential_Integral_Output1|
|
||||
|
||||
Resulting derivated/integrated signal
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_SignalDifferential_Integral_Output1|
|
||||
__________________________________________________________________
|
||||
|
||||
Settings description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SignalDifferential_Integral_Settings|
|
||||
* |OVP_DocEnd_BoxAlgorithm_SignalDifferential_Integral_Settings|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SignalDifferential_Integral_Setting1|
|
||||
|
||||
Function to be used, either a derivation or an integral
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_SignalDifferential_Integral_Setting1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SignalDifferential_Integral_Setting2|
|
||||
|
||||
The function order.
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_SignalDifferential_Integral_Setting2|
|
||||
__________________________________________________________________
|
||||
|
||||
Examples description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SignalDifferential_Integral_Examples|
|
||||
* |OVP_DocEnd_BoxAlgorithm_SignalDifferential_Integral_Examples|
|
||||
__________________________________________________________________
|
||||
|
||||
Miscellaneous description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SignalDifferential_Integral_Miscellaneous|
|
||||
* |OVP_DocEnd_BoxAlgorithm_SignalDifferential_Integral_Miscellaneous|
|
||||
*/
|
||||
+68
@@ -0,0 +1,68 @@
|
||||
/**
|
||||
* \page BoxAlgorithm_StreamSynchronization Stream Synchronization
|
||||
__________________________________________________________________
|
||||
|
||||
Detailed description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_StreamSynchronization_Description|
|
||||
This box enables you to synchronize inputs from multiple acquisition devices connected together with a hardware tagging system. Each acquisition device must translate the hardware tag into a stimulation, let's call it 'start' stimulation. The synchronisation box outputs signal only after 'start' stimulation has been received, and the time is shifted so that first data is at time 0. Plug each device on its own synchronisation box, so that the signals have the same 'start' time after these boxes.
|
||||
* |OVP_DocEnd_BoxAlgorithm_StreamSynchronization_Description|
|
||||
__________________________________________________________________
|
||||
|
||||
Inputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_StreamSynchronization_Inputs|
|
||||
|
||||
* |OVP_DocEnd_BoxAlgorithm_StreamSynchronization_Inputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_StreamSynchronization_Input1|
|
||||
The signal from the acquisition device to be synchronized.
|
||||
* |OVP_DocEnd_BoxAlgorithm_StreamSynchronization_Input1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_StreamSynchronization_Input2|
|
||||
The stimulations from the acquisition device. The 'start' stimulation, marking the beginning of the experiment, should appear in this input.
|
||||
* |OVP_DocEnd_BoxAlgorithm_StreamSynchronization_Input2|
|
||||
__________________________________________________________________
|
||||
|
||||
Outputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_StreamSynchronization_Outputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_StreamSynchronization_Outputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_StreamSynchronization_Output1|
|
||||
Time shifted signal.
|
||||
* |OVP_DocEnd_BoxAlgorithm_StreamSynchronization_Output1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_StreamSynchronization_Output2|
|
||||
Time shifted stimulations.
|
||||
* |OVP_DocEnd_BoxAlgorithm_StreamSynchronization_Output2|
|
||||
__________________________________________________________________
|
||||
|
||||
Settings description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_StreamSynchronization_Settings|
|
||||
* |OVP_DocEnd_BoxAlgorithm_StreamSynchronization_Settings|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_StreamSynchronization_Setting1|
|
||||
The ID of the stimulation which marks the beginning of the acquisition.
|
||||
* |OVP_DocEnd_BoxAlgorithm_StreamSynchronization_Setting1|
|
||||
__________________________________________________________________
|
||||
|
||||
Examples description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_StreamSynchronization_Examples|
|
||||
This box is useful if you want to use two acquisition devices at the same time. The best way to synchronize the two devices is by a physical link, this should be handled by the hardware. When experiment starts, a trigger is sent to both device through this physical link, and each device driver translate this trigger into a stimulation. As the devices received the trigger at the same time, this stimulation will have the same dating in the OpenViBE acquisition server. Finally the output of each acquisition device should pass through the synchronization box, which will re-date the signal as if it started at the moment of the reception of the stimulation.
|
||||
* |OVP_DocEnd_BoxAlgorithm_StreamSynchronization_Examples|
|
||||
__________________________________________________________________
|
||||
|
||||
Miscellaneous description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_StreamSynchronization_Miscellaneous|
|
||||
* |OVP_DocEnd_BoxAlgorithm_StreamSynchronization_Miscellaneous|
|
||||
*/
|
||||
+92
@@ -0,0 +1,92 @@
|
||||
/**
|
||||
* \page BoxAlgorithm_XDAWNTrainerDeprecated xDAWN Spatial Filter Trainer
|
||||
__________________________________________________________________
|
||||
|
||||
Detailed description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_XDAWNTrainerDeprecated_Description|
|
||||
* This box can be used in order to compute a spatial filter in order to enhance the
|
||||
* detection of evoked response potentials. In order to compute such filter, this box
|
||||
* needs to receive the whole contain of a session on the first hand, and a succession
|
||||
* of evoked response potentials on the other hand. It then computes the averaged evoked
|
||||
* response potential computes the spatial filter that makes this averaged potential
|
||||
* appear in the whole signal. This can be used e.g. for better P300 signal detection.
|
||||
*
|
||||
* It is important to consider the fact that this box will have best results for a
|
||||
* reasonably big number of input channels, possibly all over the scalp (areas where
|
||||
* the evoked response potential can not be seen will be naturally used as references
|
||||
* to reduce noise). The spatial filter results in space reduction to only keep significant
|
||||
* channels for later detection. Consider using at least 4 times more input channels than
|
||||
* the number of output channels you want. For example, reducing 16 electrodes to 3 channels
|
||||
* for P300 detection is OK.
|
||||
*
|
||||
* For more details about xDAWN, see <a href="https://www.gipsa-lab.grenoble-inp.fr/~bertrand.rivet/references/Rivet2009a.pdf">Rivet et al. 2009</a>
|
||||
* or in case this links disappears, <a href="http://www.ncbi.nlm.nih.gov/pubmed/19174332">this website</a>.
|
||||
* |OVP_DocEnd_BoxAlgorithm_XDAWNTrainerDeprecated_Description|
|
||||
__________________________________________________________________
|
||||
|
||||
Inputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_XDAWNTrainerDeprecated_Inputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_XDAWNTrainerDeprecated_Inputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_XDAWNTrainerDeprecated_Input1|
|
||||
* This input receives the experiment stimulations. As soon as the "train"
|
||||
* stimulation is received, the spatial filter is computed.
|
||||
* |OVP_DocEnd_BoxAlgorithm_XDAWNTrainerDeprecated_Input1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_XDAWNTrainerDeprecated_Input2|
|
||||
* This input should receive the whole signal of the session.
|
||||
* |OVP_DocEnd_BoxAlgorithm_XDAWNTrainerDeprecated_Input2|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_XDAWNTrainerDeprecated_Input3|
|
||||
* This input should receive the multiple evoked response potentials.
|
||||
* |OVP_DocEnd_BoxAlgorithm_XDAWNTrainerDeprecated_Input3|
|
||||
__________________________________________________________________
|
||||
|
||||
Outputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_CSPSpatialFilterTrainer_Outputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_CSPSpatialFilterTrainer_Outputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_CSPSpatialFilterTrainer_Output1|
|
||||
The xDAWN Trainer outputs the stimulation <b>OVTK_StimulationId_TrainCompleted</b> when the training process was successful. No output is produced if the process failed.
|
||||
* |OVP_DocEnd_BoxAlgorithm_CSPSpatialFilterTrainer_Output1|
|
||||
__________________________________________________________________
|
||||
|
||||
Settings description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_XDAWNTrainerDeprecated_Settings|
|
||||
* |OVP_DocEnd_BoxAlgorithm_XDAWNTrainerDeprecated_Settings|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_XDAWNTrainerDeprecated_Setting1|
|
||||
* This setting contains the stimulation to use to trigger the training process.
|
||||
* |OVP_DocEnd_BoxAlgorithm_XDAWNTrainerDeprecated_Setting1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_XDAWNTrainerDeprecated_Setting2|
|
||||
* This setting tells the box what configuration file to generate. This configuration file can
|
||||
* be used to set the correct values of a \ref Doc_BoxAlgorithm_SpatialFilter box.
|
||||
* |OVP_DocEnd_BoxAlgorithm_XDAWNTrainerDeprecated_Setting2|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_XDAWNTrainerDeprecated_Setting3|
|
||||
* This setting tells how many dimension should be kept out of the spatial filter.
|
||||
* |OVP_DocEnd_BoxAlgorithm_XDAWNTrainerDeprecated_Setting3|
|
||||
__________________________________________________________________
|
||||
|
||||
Examples description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_XDAWNTrainerDeprecated_Examples|
|
||||
* |OVP_DocEnd_BoxAlgorithm_XDAWNTrainerDeprecated_Examples|
|
||||
__________________________________________________________________
|
||||
|
||||
Miscellaneous description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_XDAWNTrainerDeprecated_Miscellaneous|
|
||||
* |OVP_DocEnd_BoxAlgorithm_XDAWNTrainerDeprecated_Miscellaneous|
|
||||
*/
|
||||
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|
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|
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+143
@@ -0,0 +1,143 @@
|
||||
#if defined(TARGET_HAS_ThirdPartyEIGEN)
|
||||
|
||||
#include "ovpCAlgorithmARBurgMethod.h"
|
||||
#include <iostream>
|
||||
#include <sstream>
|
||||
|
||||
#include <Eigen/Dense>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
bool CAlgorithmARBurgMethod::initialize()
|
||||
{
|
||||
ip_pMatrix.initialize(this->getInputParameter(OVP_Algorithm_ARBurgMethod_InputParameterId_Matrix));
|
||||
op_pMatrix.initialize(this->getOutputParameter(OVP_Algorithm_ARBurgMethod_OutputParameterId_Matrix));
|
||||
ip_Order.initialize(this->getInputParameter(OVP_Algorithm_ARBurgMethod_InputParameterId_UInteger));
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CAlgorithmARBurgMethod::uninitialize()
|
||||
{
|
||||
op_pMatrix.uninitialize();
|
||||
ip_pMatrix.uninitialize();
|
||||
ip_Order.uninitialize();
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CAlgorithmARBurgMethod::process()
|
||||
{
|
||||
m_order = size_t(ip_Order);
|
||||
|
||||
const size_t nChannel = ip_pMatrix->getDimensionSize(0);
|
||||
const size_t samplesPerChannel = ip_pMatrix->getDimensionSize(1);
|
||||
|
||||
CMatrix* iMatrix = ip_pMatrix;
|
||||
CMatrix* oMatrix = op_pMatrix;
|
||||
|
||||
if (this->isInputTriggerActive(OVP_Algorithm_ARBurgMethod_InputTriggerId_Initialize))
|
||||
{
|
||||
if (iMatrix->getDimensionCount() != 2)
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "The input matrix must have 2 dimensions";
|
||||
return false;
|
||||
}
|
||||
|
||||
if (iMatrix->getDimensionSize(1) < 2 * m_order)
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "The input vector must be greater than twice the order";
|
||||
return false;
|
||||
}
|
||||
|
||||
// Setting size of output
|
||||
|
||||
oMatrix->resize(nChannel, m_order + 1); // The number of coefficients per channel is equal to the order + 1
|
||||
|
||||
for (size_t i = 0; i < nChannel; ++i)
|
||||
{
|
||||
const std::string label = "Channel " + std::to_string(i + 1);
|
||||
oMatrix->setDimensionLabel(0, i, label);
|
||||
}
|
||||
for (size_t i = 0; i < (m_order + 1); ++i)
|
||||
{
|
||||
const std::string label = "ARCoeff " + std::to_string(i + 1);
|
||||
oMatrix->setDimensionLabel(1, i, label);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
if (this->isInputTriggerActive(OVP_Algorithm_ARBurgMethod_InputTriggerId_Process))
|
||||
{
|
||||
// Compute the coefficients for each channel
|
||||
for (size_t j = 0; j < nChannel; ++j)
|
||||
{
|
||||
// Initialization of all needed vectors
|
||||
|
||||
m_errForwardPrediction = Eigen::RowVectorXd::Zero(samplesPerChannel); // Error Forward prediction
|
||||
m_errBackwardPrediction = Eigen::RowVectorXd::Zero(samplesPerChannel); //Error Backward prediction
|
||||
|
||||
m_errForward = Eigen::RowVectorXd::Zero(samplesPerChannel); // Error Forward
|
||||
m_errBackward = Eigen::RowVectorXd::Zero(samplesPerChannel); // Error Backward
|
||||
|
||||
m_arCoefs = Eigen::RowVectorXd::Zero(m_order + 1); // Vector containing the AR coefficients for each channel, it will be our output vector
|
||||
m_error = Eigen::RowVectorXd::Zero(m_order + 1); // Total error
|
||||
|
||||
m_k = 0.0;
|
||||
m_arCoefs(0) = 1.0;
|
||||
|
||||
Eigen::VectorXd arReversed;
|
||||
arReversed = Eigen::VectorXd::Zero(m_order + 1);
|
||||
|
||||
// Retrieving input datas
|
||||
for (size_t i = 0; i < samplesPerChannel; ++i)
|
||||
{
|
||||
m_errForward(i) = iMatrix->getBuffer()[i + j * (samplesPerChannel)]; // Error Forward is the input matrix at first
|
||||
m_errBackward(i) = iMatrix->getBuffer()[i + j * (samplesPerChannel)]; //Error Backward is the input matrix at first
|
||||
|
||||
m_error(0) += (iMatrix->getBuffer()[i + j * (samplesPerChannel)] * iMatrix->getBuffer()[i + j * (samplesPerChannel)]) / samplesPerChannel;
|
||||
}
|
||||
|
||||
// we iterate over the order
|
||||
for (size_t n = 1; n <= m_order; ++n)
|
||||
{
|
||||
const size_t length = samplesPerChannel - n;
|
||||
|
||||
m_errForwardPrediction.resize(length);
|
||||
m_errBackwardPrediction.resize(length);
|
||||
|
||||
m_errForwardPrediction = m_errForward.tail(length);
|
||||
m_errBackwardPrediction = m_errBackward.head(length);
|
||||
|
||||
const double num = -2.0 * m_errBackwardPrediction.dot(m_errForwardPrediction);
|
||||
const double den = (m_errForwardPrediction.dot(m_errForwardPrediction)) + (m_errBackwardPrediction.dot(m_errBackwardPrediction));
|
||||
|
||||
m_k = num / den;
|
||||
|
||||
// Update errors forward and backward vectors
|
||||
|
||||
m_errForward = m_errForwardPrediction + m_k * m_errBackwardPrediction;
|
||||
m_errBackward = m_errBackwardPrediction + m_k * m_errForwardPrediction;
|
||||
|
||||
// Compute the AR coefficients
|
||||
|
||||
for (size_t i = 1; i <= n; ++i) { arReversed(i) = m_arCoefs(n - i); }
|
||||
|
||||
m_arCoefs = m_arCoefs + m_k * arReversed;
|
||||
|
||||
// Update Total Error
|
||||
m_error(n) = (1 - m_k * m_k) * m_error(n - 1);
|
||||
}
|
||||
for (size_t i = 0; i <= m_order; ++i) { oMatrix->getBuffer()[i + j * (m_order + 1)] = m_arCoefs(i); }
|
||||
}
|
||||
this->activateOutputTrigger(OVP_Algorithm_ARBurgMethod_OutputTriggerId_ProcessDone, true);
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
#endif // TARGET_HAS_ThirdPartyEIGEN
|
||||
+84
@@ -0,0 +1,84 @@
|
||||
# pragma once
|
||||
|
||||
#if defined(TARGET_HAS_ThirdPartyEIGEN)
|
||||
|
||||
#include "../../ovp_defines.h"
|
||||
#include <openvibe/ov_all.h>
|
||||
#include <toolkit/ovtk_all.h>
|
||||
|
||||
#include <Eigen/Dense>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
class CAlgorithmARBurgMethod final : public Toolkit::TAlgorithm<IAlgorithm>
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { delete this; }
|
||||
|
||||
bool initialize() override;
|
||||
bool uninitialize() override;
|
||||
bool process() override;
|
||||
|
||||
_IsDerivedFromClass_Final_(Toolkit::TAlgorithm<IAlgorithm>, OVP_ClassId_Algorithm_ARBurgMethod)
|
||||
|
||||
protected:
|
||||
|
||||
Kernel::TParameterHandler<CMatrix*> ip_pMatrix; // input matrix
|
||||
Kernel::TParameterHandler<CMatrix*> op_pMatrix; // output matrix
|
||||
Kernel::TParameterHandler<uint64_t> ip_Order;
|
||||
|
||||
private:
|
||||
|
||||
Eigen::VectorXd m_errForward; // Error Forward
|
||||
Eigen::VectorXd m_errBackward; //Error Backward
|
||||
Eigen::VectorXd m_arCoefs; // AutoRegressive Coefficents
|
||||
|
||||
Eigen::VectorXd m_errForwardPrediction; // Error Forward prediction
|
||||
Eigen::VectorXd m_errBackwardPrediction; //Error Backward prediction
|
||||
|
||||
Eigen::VectorXd m_error; // Total error vector
|
||||
|
||||
double m_k = 0;
|
||||
size_t m_order = 0;
|
||||
};
|
||||
|
||||
class CAlgorithmARBurgMethodDesc final : public IAlgorithmDesc
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { }
|
||||
|
||||
CString getName() const override { return CString("AR Burg's Method algorithm"); }
|
||||
CString getAuthorName() const override { return CString("Alison Cellard"); }
|
||||
CString getAuthorCompanyName() const override { return CString("INRIA"); }
|
||||
CString getShortDescription() const override { return CString("Extract AR coefficient using Burg's Method"); }
|
||||
CString getDetailedDescription() const override { return CString(""); }
|
||||
CString getCategory() const override { return CString("Signal Processing"); }
|
||||
CString getVersion() const override { return CString("1.0"); }
|
||||
virtual CString getStockItemName() const { return CString("gtk-execute"); }
|
||||
|
||||
CIdentifier getCreatedClass() const override { return OVP_ClassId_Algorithm_ARBurgMethod; }
|
||||
IPluginObject* create() override { return new CAlgorithmARBurgMethod; }
|
||||
|
||||
bool getAlgorithmPrototype(Kernel::IAlgorithmProto& prototype) const override
|
||||
{
|
||||
prototype.addInputParameter(OVP_Algorithm_ARBurgMethod_InputParameterId_Matrix, "Vector", Kernel::ParameterType_Matrix);
|
||||
prototype.addOutputParameter(OVP_Algorithm_ARBurgMethod_OutputParameterId_Matrix, "Coefficents Vector", Kernel::ParameterType_Matrix);
|
||||
prototype.addInputParameter(OVP_Algorithm_ARBurgMethod_InputParameterId_UInteger, "Order", Kernel::ParameterType_UInteger);
|
||||
|
||||
prototype.addInputTrigger(OVP_Algorithm_ARBurgMethod_InputTriggerId_Initialize, "Initialize");
|
||||
prototype.addInputTrigger(OVP_Algorithm_ARBurgMethod_InputTriggerId_Process, "Process");
|
||||
prototype.addOutputTrigger(OVP_Algorithm_ARBurgMethod_OutputTriggerId_ProcessDone, "Process done");
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(IAlgorithmDesc, OVP_ClassId_Algorithm_ARBurgMethodDesc)
|
||||
};
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
|
||||
#endif // TARGET_HAS_ThirdPartyEIGEN
|
||||
+147
@@ -0,0 +1,147 @@
|
||||
#if defined(TARGET_HAS_ThirdPartyEIGEN)
|
||||
|
||||
#include "ovpCHilbertTransform.h"
|
||||
#include <complex>
|
||||
#include <Eigen/Dense>
|
||||
#include <unsupported/Eigen/FFT>
|
||||
|
||||
bool HilbertTransform::transform(const Eigen::VectorXcd& in, Eigen::VectorXcd& out)
|
||||
{
|
||||
const size_t nSamples = in.size();
|
||||
|
||||
// Resize our buffers if input size has changed
|
||||
if (size_t(m_signalFourier.size()) != nSamples)
|
||||
{
|
||||
m_signalFourier = Eigen::VectorXcd::Zero(nSamples);
|
||||
m_hilbert = Eigen::VectorXcd::Zero(nSamples);
|
||||
|
||||
//Initialization of vector h used to compute analytic signal
|
||||
m_hilbert(0) = 1.0;
|
||||
|
||||
if (nSamples % 2 == 0)
|
||||
{
|
||||
m_hilbert(nSamples / 2) = 1.0;
|
||||
m_hilbert.segment(1, (nSamples / 2) - 1).setOnes();
|
||||
m_hilbert.segment(1, (nSamples / 2) - 1) *= 2.0;
|
||||
m_hilbert.tail((nSamples / 2) + 1).setZero();
|
||||
}
|
||||
else
|
||||
{
|
||||
m_hilbert((nSamples + 1) / 2) = 1.0;
|
||||
m_hilbert.segment(1, (nSamples / 2)).setOnes();
|
||||
m_hilbert.segment(1, (nSamples / 2)) *= 2.0;
|
||||
m_hilbert.tail(((nSamples + 1) / 2) + 1).setZero();
|
||||
}
|
||||
}
|
||||
|
||||
// Always resize output for safety
|
||||
out.resize(nSamples);
|
||||
|
||||
//Fast Fourier Transform of input signal
|
||||
m_fft.fwd(m_signalFourier, in);
|
||||
|
||||
//Apply Hilbert transform by element-wise multiplying fft vector by h
|
||||
m_signalFourier = m_signalFourier.cwiseProduct(m_hilbert);
|
||||
|
||||
//Inverse Fast Fourier transform
|
||||
m_fft.inv(out, m_signalFourier); // m_vecXcdSignalBuffer is now the analytical signal of the initial input signal
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
bool CAlgorithmHilbertTransform::initialize()
|
||||
{
|
||||
ip_matrix.initialize(this->getInputParameter(OVP_Algorithm_HilbertTransform_InputParameterId_Matrix));
|
||||
op_hilbertMatrix.initialize(this->getOutputParameter(OVP_Algorithm_HilbertTransform_OutputParameterId_HilbertMatrix));
|
||||
op_envelopeMatrix.initialize(this->getOutputParameter(OVP_Algorithm_HilbertTransform_OutputParameterId_EnvelopeMatrix));
|
||||
op_phaseMatrix.initialize(this->getOutputParameter(OVP_Algorithm_HilbertTransform_OutputParameterId_PhaseMatrix));
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CAlgorithmHilbertTransform::uninitialize()
|
||||
{
|
||||
op_hilbertMatrix.uninitialize();
|
||||
op_envelopeMatrix.uninitialize();
|
||||
op_phaseMatrix.uninitialize();
|
||||
ip_matrix.uninitialize();
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CAlgorithmHilbertTransform::process()
|
||||
{
|
||||
const size_t nChannel = ip_matrix->getDimensionSize(0);
|
||||
const size_t samplesPerChannel = ip_matrix->getDimensionSize(1);
|
||||
|
||||
CMatrix* matrix = ip_matrix;
|
||||
CMatrix* hilbert = op_hilbertMatrix;
|
||||
CMatrix* envelope = op_envelopeMatrix;
|
||||
CMatrix* phase = op_phaseMatrix;
|
||||
|
||||
if (this->isInputTriggerActive(OVP_Algorithm_HilbertTransform_InputTriggerId_Initialize)) //Check if the input is correct
|
||||
{
|
||||
if (matrix->getDimensionCount() != 2)
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "The input matrix must have 2 dimensions, here the dimension is " << matrix->getDimensionCount()
|
||||
<< "\n";
|
||||
return false;
|
||||
}
|
||||
|
||||
if (matrix->getDimensionSize(1) < 2)
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "Can't compute Hilbert transform on data length " << matrix->getDimensionSize(1) << "\n";
|
||||
return false;
|
||||
}
|
||||
|
||||
//Setting size of outputs
|
||||
|
||||
hilbert->resize(nChannel, samplesPerChannel);
|
||||
envelope->resize(nChannel, samplesPerChannel);
|
||||
phase->resize(nChannel, samplesPerChannel);
|
||||
|
||||
for (size_t i = 0; i < nChannel; ++i)
|
||||
{
|
||||
hilbert->setDimensionLabel(0, i, matrix->getDimensionLabel(0, i));
|
||||
envelope->setDimensionLabel(0, i, matrix->getDimensionLabel(0, i));
|
||||
phase->setDimensionLabel(0, i, matrix->getDimensionLabel(0, i));
|
||||
}
|
||||
}
|
||||
|
||||
if (this->isInputTriggerActive(OVP_Algorithm_HilbertTransform_InputTriggerId_Process))
|
||||
{
|
||||
//Compute Hilbert transform for each channel separately
|
||||
for (size_t c = 0; c < nChannel; ++c)
|
||||
{
|
||||
// We cannot do a simple ptr assignment here as we need to convert real input to a complex vector
|
||||
Eigen::VectorXcd vecXcdSingleChannel = Eigen::VectorXcd::Zero(samplesPerChannel);
|
||||
const double* buffer = &matrix->getBuffer()[c * samplesPerChannel];
|
||||
for (size_t samples = 0; samples < samplesPerChannel; ++samples)
|
||||
{
|
||||
vecXcdSingleChannel(samples) = buffer[samples];
|
||||
vecXcdSingleChannel(samples).imag(0.0);
|
||||
}
|
||||
|
||||
Eigen::VectorXcd vecXcdSingleChannelTransformed;
|
||||
m_hilbert.transform(vecXcdSingleChannel, vecXcdSingleChannelTransformed);
|
||||
|
||||
//Compute envelope and phase and pass them to the corresponding outputs
|
||||
for (size_t s = 0; s < samplesPerChannel; ++s)
|
||||
{
|
||||
hilbert->getBuffer()[s + c * samplesPerChannel] = vecXcdSingleChannelTransformed(s).imag();
|
||||
envelope->getBuffer()[s + c * samplesPerChannel] = abs(vecXcdSingleChannelTransformed(s));
|
||||
phase->getBuffer()[s + c * samplesPerChannel] = arg(vecXcdSingleChannelTransformed(s));
|
||||
}
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
#endif //TARGET_HAS_ThirdPartyEIGEN
|
||||
+93
@@ -0,0 +1,93 @@
|
||||
#pragma once
|
||||
|
||||
#if defined(TARGET_HAS_ThirdPartyEIGEN)
|
||||
|
||||
#include "../../ovp_defines.h"
|
||||
#include <openvibe/ov_all.h>
|
||||
#include <toolkit/ovtk_all.h>
|
||||
#include <Eigen/Dense>
|
||||
#include <unsupported/Eigen/FFT>
|
||||
|
||||
// This class could be in its own file
|
||||
class HilbertTransform
|
||||
{
|
||||
public:
|
||||
|
||||
bool transform(const Eigen::VectorXcd& in, Eigen::VectorXcd& out);
|
||||
|
||||
private:
|
||||
Eigen::VectorXcd m_signalFourier; // Fourier Transform of the input signal
|
||||
Eigen::VectorXcd m_hilbert; // Vector h used to apply Hilbert transform
|
||||
|
||||
Eigen::FFT<double, Eigen::internal::kissfft_impl<double>> m_fft; // Instance of the fft transform
|
||||
};
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
class CAlgorithmHilbertTransform final : public Toolkit::TAlgorithm<IAlgorithm>
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { delete this; }
|
||||
|
||||
bool initialize() override;
|
||||
bool uninitialize() override;
|
||||
bool process() override;
|
||||
|
||||
_IsDerivedFromClass_Final_(Toolkit::TAlgorithm<IAlgorithm>, OVP_ClassId_Algorithm_HilbertTransform)
|
||||
|
||||
|
||||
protected:
|
||||
|
||||
Kernel::TParameterHandler<CMatrix*> ip_matrix; //input matrix
|
||||
Kernel::TParameterHandler<CMatrix*> op_hilbertMatrix; //output matrix 1 : Hilbert transform of the signal
|
||||
Kernel::TParameterHandler<CMatrix*> op_envelopeMatrix; //output matrix 2 : Envelope of the signal
|
||||
Kernel::TParameterHandler<CMatrix*> op_phaseMatrix; //output matrix 3 : Phase of the signal
|
||||
|
||||
HilbertTransform m_hilbert; // Instance of the Hilbert transform doing the actual computation
|
||||
};
|
||||
|
||||
class CAlgorithmHilbertTransformDesc final : public IAlgorithmDesc
|
||||
{
|
||||
public:
|
||||
void release() override { }
|
||||
|
||||
CString getName() const override { return CString("Hilbert Transform"); }
|
||||
CString getAuthorName() const override { return CString("Alison Cellard"); }
|
||||
CString getAuthorCompanyName() const override { return CString("Inria"); }
|
||||
CString getShortDescription() const override { return CString("Computes the Hilbert transform of a signal"); }
|
||||
|
||||
CString getDetailedDescription() const override
|
||||
{
|
||||
return CString("Give the analytic signal ua(t) = u(t) + iH(u(t)) of the input signal u(t) using Hilbert transform");
|
||||
}
|
||||
|
||||
CString getCategory() const override { return CString("Signal processing/Basic"); }
|
||||
CString getVersion() const override { return CString("0.2"); }
|
||||
virtual CString getStockItemName() const { return CString("gtk-execute"); }
|
||||
|
||||
CIdentifier getCreatedClass() const override { return OVP_ClassId_Algorithm_HilbertTransform; }
|
||||
IPluginObject* create() override { return new CAlgorithmHilbertTransform; }
|
||||
|
||||
bool getAlgorithmPrototype(Kernel::IAlgorithmProto& prototype) const override
|
||||
{
|
||||
prototype.addInputParameter(OVP_Algorithm_HilbertTransform_InputParameterId_Matrix, "Matrix", Kernel::ParameterType_Matrix);
|
||||
prototype.addOutputParameter(OVP_Algorithm_HilbertTransform_OutputParameterId_HilbertMatrix, "Hilbert Matrix", Kernel::ParameterType_Matrix);
|
||||
prototype.addOutputParameter(OVP_Algorithm_HilbertTransform_OutputParameterId_EnvelopeMatrix, "Envelope Matrix", Kernel::ParameterType_Matrix);
|
||||
prototype.addOutputParameter(OVP_Algorithm_HilbertTransform_OutputParameterId_PhaseMatrix, "Phase Matrix", Kernel::ParameterType_Matrix);
|
||||
|
||||
prototype.addInputTrigger(OVP_Algorithm_HilbertTransform_InputTriggerId_Initialize, "Initialize");
|
||||
prototype.addInputTrigger(OVP_Algorithm_HilbertTransform_InputTriggerId_Process, "Process");
|
||||
prototype.addOutputTrigger(OVP_Algorithm_HilbertTransform_OutputTriggerId_ProcessDone, "Process done");
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(IAlgorithmDesc, OVP_ClassId_Algorithm_HilbertTransformDesc)
|
||||
};
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
|
||||
#endif //TARGET_HAS_ThirdPartyEIGEN
|
||||
+230
@@ -0,0 +1,230 @@
|
||||
#if defined(TARGET_HAS_ThirdPartyITPP)
|
||||
|
||||
#include "ovpCMatrixVariance.h"
|
||||
|
||||
#include <cmath>
|
||||
|
||||
// the boost version used at the moment of writing this caused 4800 by internal call to "int _isnan" in a bool-returning function.
|
||||
#if defined(WIN32)
|
||||
#pragma warning (disable : 4800)
|
||||
#endif
|
||||
|
||||
#include <boost/math/distributions/students_t.hpp>
|
||||
#include <itpp/base/vec.h>
|
||||
#include <itpp/base/math/elem_math.h>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
bool CMatrixVariance::initialize()
|
||||
{
|
||||
ip_averagingMethod.initialize(getInputParameter(OVP_Algorithm_MatrixVariance_InputParameterId_AveragingMethod));
|
||||
ip_matrixCount.initialize(getInputParameter(OVP_Algorithm_MatrixVariance_InputParameterId_MatrixCount));
|
||||
ip_significanceLevel.initialize(getInputParameter(OVP_Algorithm_MatrixVariance_InputParameterId_SignificanceLevel));
|
||||
ip_matrix.initialize(getInputParameter(OVP_Algorithm_MatrixVariance_InputParameterId_Matrix));
|
||||
op_averagedMatrix.initialize(getOutputParameter(OVP_Algorithm_MatrixVariance_OutputParameterId_AveragedMatrix));
|
||||
op_varianceMatrix.initialize(getOutputParameter(OVP_Algorithm_MatrixVariance_OutputParameterId_Variance));
|
||||
op_confidenceBound.initialize(getOutputParameter(OVP_Algorithm_MatrixVariance_OutputParameterId_ConfidenceBound));
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CMatrixVariance::uninitialize()
|
||||
{
|
||||
for (auto it = m_history.begin(); it != m_history.end(); ++it) { delete *it; }
|
||||
m_history.clear();
|
||||
|
||||
op_averagedMatrix.uninitialize();
|
||||
op_varianceMatrix.uninitialize();
|
||||
op_confidenceBound.uninitialize();
|
||||
ip_matrix.uninitialize();
|
||||
ip_matrixCount.uninitialize();
|
||||
ip_averagingMethod.uninitialize();
|
||||
ip_significanceLevel.uninitialize();
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
// ________________________________________________________________________________________________________________
|
||||
//
|
||||
|
||||
bool CMatrixVariance::process()
|
||||
{
|
||||
CMatrix* iMatrix = ip_matrix;
|
||||
//CMatrix* oMatrix=op_pAveragedMatrix;
|
||||
|
||||
bool shouldPerformAverage = false;
|
||||
|
||||
if (this->isInputTriggerActive(OVP_Algorithm_MatrixVariance_InputTriggerId_Reset))
|
||||
{
|
||||
m_mean.set_size(int(ip_matrix->getBufferElementCount()));
|
||||
m_mean.zeros();
|
||||
m_m.set_size(int(ip_matrix->getBufferElementCount()));
|
||||
m_m.zeros();
|
||||
m_variance.set_size(int(ip_matrix->getBufferElementCount()));
|
||||
m_variance.zeros();
|
||||
m_inputCounter = 0;
|
||||
|
||||
for (auto it = m_history.begin(); it != m_history.end(); ++it) { delete *it; }
|
||||
m_history.clear();
|
||||
|
||||
op_averagedMatrix->copyDescription(*iMatrix);
|
||||
op_varianceMatrix->copyDescription(*iMatrix);
|
||||
op_confidenceBound->copyDescription(*iMatrix);
|
||||
}
|
||||
|
||||
if (this->isInputTriggerActive(OVP_Algorithm_MatrixVariance_InputTriggerId_FeedMatrix))
|
||||
{
|
||||
const int nElement = int(iMatrix->getBufferElementCount());
|
||||
if (ip_averagingMethod == size_t(EEpochAverageMethod::Moving))
|
||||
{
|
||||
//CMatrix* swapMatrix= nullptr;
|
||||
|
||||
if (m_history.size() >= ip_matrixCount)
|
||||
{
|
||||
delete m_history.front();
|
||||
m_history.pop_front();
|
||||
}
|
||||
/*else
|
||||
{
|
||||
swapMatrix=new CMatrix();
|
||||
swapMatrix->copyDescription(*iMatrix);
|
||||
}*/
|
||||
//swapMatrix->copyContent(*iMatrix);
|
||||
|
||||
itpp::Vec<double>* matrices = new itpp::Vec<double>(iMatrix->getBuffer(), nElement);
|
||||
m_history.push_back(matrices);
|
||||
shouldPerformAverage = (m_history.size() == ip_matrixCount);
|
||||
}
|
||||
else if (ip_averagingMethod == size_t(EEpochAverageMethod::MovingImmediate))
|
||||
{
|
||||
//CMatrix* swapMatrix= nullptr;
|
||||
|
||||
if (m_history.size() >= ip_matrixCount)
|
||||
{
|
||||
delete m_history.front();
|
||||
m_history.pop_front();
|
||||
}
|
||||
/*else
|
||||
{
|
||||
swapMatrix=new CMatrix();
|
||||
swapMatrix->copyDescription(*iMatrix);
|
||||
}*/
|
||||
|
||||
//swapMatrix->copyContent(*iMatrix);
|
||||
|
||||
itpp::Vec<double>* matrices = new itpp::Vec<double>(iMatrix->getBuffer(), nElement);
|
||||
m_history.push_back(matrices);
|
||||
shouldPerformAverage = (!m_history.empty());
|
||||
}
|
||||
else if (ip_averagingMethod == size_t(EEpochAverageMethod::Block))
|
||||
{
|
||||
//CMatrix* swapMatrix=new CMatrix();
|
||||
|
||||
if (m_history.size() >= ip_matrixCount)
|
||||
{
|
||||
for (auto it = m_history.begin(); it != m_history.end(); ++it) { delete *it; }
|
||||
m_history.clear();
|
||||
}
|
||||
|
||||
//swapMatrix->copy(*iMatrix);
|
||||
|
||||
itpp::Vec<double>* matrices = new itpp::Vec<double>(iMatrix->getBuffer(), nElement);
|
||||
m_history.push_back(matrices);
|
||||
shouldPerformAverage = (m_history.size() == ip_matrixCount);
|
||||
}
|
||||
else if (ip_averagingMethod == size_t(EEpochAverageMethod::Cumulative))
|
||||
{
|
||||
if (!m_history.empty())
|
||||
{
|
||||
//std::cout << "size of history " << m_history.size() << "\n";
|
||||
delete m_history.front();
|
||||
m_history.pop_front();
|
||||
}
|
||||
//else { std::cout << "history empty \n"; }
|
||||
//CMatrix* swapMatrix=new CMatrix();
|
||||
//swapMatrix->copy(*iMatrix);
|
||||
|
||||
itpp::Vec<double>* matrices = new itpp::Vec<double>(iMatrix->getBuffer(), nElement);
|
||||
m_history.push_back(matrices);
|
||||
shouldPerformAverage = (!m_history.empty());
|
||||
}
|
||||
else { shouldPerformAverage = false; }
|
||||
}
|
||||
|
||||
if (shouldPerformAverage)
|
||||
{
|
||||
if (!m_history.empty())
|
||||
{
|
||||
boost::math::students_t_distribution<double> distrib(2);
|
||||
if (ip_averagingMethod == size_t(EEpochAverageMethod::Cumulative))
|
||||
{
|
||||
//incremental estimation of mean and variance
|
||||
for (auto it = m_history.begin(); it != m_history.end(); ++it)
|
||||
{
|
||||
m_inputCounter++;
|
||||
itpp::Vec<double> buffer = **it;
|
||||
itpp::Vec<double> delta = buffer - m_mean;
|
||||
m_mean += delta / double(m_inputCounter);
|
||||
m_m += elem_mult(delta, (buffer - m_mean));
|
||||
if (m_inputCounter > 1) { m_variance = m_m / double(m_inputCounter - 1); }
|
||||
}
|
||||
distrib = boost::math::students_t_distribution<double>(m_inputCounter <= 1 ? 1 : m_inputCounter - 1);
|
||||
//CMatrix swapMatrix();
|
||||
//getLogManager() << Kernel::LogLevel_Info << "Variance first element " << m_Variance[0] << ", last element " << m_Variance[iMatrix->getBufferElementCount()-1] << "\n";
|
||||
|
||||
memcpy(op_averagedMatrix->getBuffer(), m_mean._data(), iMatrix->getBufferElementCount() * sizeof(double));
|
||||
memcpy(op_varianceMatrix->getBuffer(), m_variance._data(), iMatrix->getBufferElementCount() * sizeof(double));
|
||||
}
|
||||
else
|
||||
{
|
||||
distrib = boost::math::students_t_distribution<double>(double(ip_matrixCount) - 1);
|
||||
|
||||
op_varianceMatrix->resetBuffer();
|
||||
op_averagedMatrix->resetBuffer();
|
||||
|
||||
const size_t count = op_averagedMatrix->getBufferElementCount();
|
||||
const double scale = 1. / m_history.size();
|
||||
|
||||
for (auto& h : m_history)
|
||||
{
|
||||
//batch computation of mean
|
||||
itpp::Vec<double> buffer = *h;
|
||||
double* averageBuffer = op_averagedMatrix->getBuffer();
|
||||
for (int i = 0; i < int(count); ++i)
|
||||
{
|
||||
*averageBuffer += buffer[i] * scale;
|
||||
averageBuffer++;
|
||||
}
|
||||
//batch computation of variance
|
||||
averageBuffer = op_averagedMatrix->getBuffer();
|
||||
double* matrixVarianceBuffer = op_varianceMatrix->getBuffer();
|
||||
for (int i = 0; i < int(count); ++i)
|
||||
{
|
||||
*matrixVarianceBuffer += (buffer[i] - *(averageBuffer + i)) * (buffer[i] - *(averageBuffer + i)) / (m_history.size() - 1.0F);
|
||||
matrixVarianceBuffer++;
|
||||
}
|
||||
}
|
||||
m_variance = itpp::Vec<double>(op_averagedMatrix->getBuffer(), int(count));
|
||||
}
|
||||
|
||||
//computing confidence bounds
|
||||
const double q = double(quantile(complement(distrib, ip_significanceLevel / 2.0)));
|
||||
getLogManager() << Kernel::LogLevel_Debug << "Quantile at " << ip_significanceLevel << " is " << q << "\n";
|
||||
itpp::Vec<double> bound;
|
||||
if (ip_averagingMethod == size_t(EEpochAverageMethod::Cumulative)) { bound = (q / sqrt(double(m_inputCounter))) * itpp::sqrt(m_variance); }
|
||||
else { bound = (q / double(ip_matrixCount)) * itpp::sqrt(m_variance); }
|
||||
memcpy(op_confidenceBound->getBuffer(), bound._data(), iMatrix->getBufferElementCount() * sizeof(double));
|
||||
}
|
||||
|
||||
this->activateOutputTrigger(OVP_Algorithm_MatrixVariance_OutputTriggerId_AveragePerformed, true);
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
#endif
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+90
@@ -0,0 +1,90 @@
|
||||
#pragma once
|
||||
|
||||
#if defined(TARGET_HAS_ThirdPartyITPP)
|
||||
|
||||
#include "../../ovp_defines.h"
|
||||
|
||||
#include <openvibe/ov_all.h>
|
||||
#include <toolkit/ovtk_all.h>
|
||||
|
||||
#include <itpp/base/vec.h>
|
||||
#include <deque>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
class CMatrixVariance final : public Toolkit::TAlgorithm<IAlgorithm>
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { delete this; }
|
||||
|
||||
bool initialize() override;
|
||||
bool uninitialize() override;
|
||||
|
||||
bool process() override;
|
||||
|
||||
_IsDerivedFromClass_Final_(Toolkit::TAlgorithm<IAlgorithm>, OVP_ClassId_Algorithm_MatrixVariance)
|
||||
|
||||
protected:
|
||||
|
||||
Kernel::TParameterHandler<uint64_t> ip_averagingMethod;
|
||||
Kernel::TParameterHandler<uint64_t> ip_matrixCount;
|
||||
Kernel::TParameterHandler<double> ip_significanceLevel;
|
||||
Kernel::TParameterHandler<CMatrix*> ip_matrix;
|
||||
Kernel::TParameterHandler<CMatrix*> op_averagedMatrix;
|
||||
Kernel::TParameterHandler<CMatrix*> op_varianceMatrix;
|
||||
Kernel::TParameterHandler<CMatrix*> op_confidenceBound;
|
||||
|
||||
std::deque<itpp::Vec<double>*> m_history;
|
||||
|
||||
itpp::Vec<double> m_mean;
|
||||
itpp::Vec<double> m_m;
|
||||
itpp::Vec<double> m_variance;
|
||||
size_t m_inputCounter = 0;
|
||||
};
|
||||
|
||||
class CMatrixVarianceDesc final : public IAlgorithmDesc
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { }
|
||||
|
||||
CString getName() const override { return CString("Matrix variance"); }
|
||||
CString getAuthorName() const override { return CString("Dieter Devlaminck"); }
|
||||
CString getAuthorCompanyName() const override { return CString("INRIA"); }
|
||||
CString getShortDescription() const override { return CString(""); }
|
||||
CString getDetailedDescription() const override { return CString(""); }
|
||||
CString getCategory() const override { return CString("Signal processing/Basic"); }
|
||||
CString getVersion() const override { return CString("1.0"); }
|
||||
|
||||
CIdentifier getCreatedClass() const override { return OVP_ClassId_Algorithm_MatrixVariance; }
|
||||
IPluginObject* create() override { return new CMatrixVariance(); }
|
||||
|
||||
bool getAlgorithmPrototype(Kernel::IAlgorithmProto& prototype) const override
|
||||
{
|
||||
prototype.addInputParameter(OVP_Algorithm_MatrixVariance_InputParameterId_Matrix, "Matrix", Kernel::ParameterType_Matrix);
|
||||
prototype.addInputParameter(OVP_Algorithm_MatrixVariance_InputParameterId_MatrixCount, "Matrix count", Kernel::ParameterType_UInteger);
|
||||
prototype.addInputParameter(OVP_Algorithm_MatrixVariance_InputParameterId_SignificanceLevel, "Significance Level", Kernel::ParameterType_UInteger);
|
||||
prototype.addInputParameter(OVP_Algorithm_MatrixVariance_InputParameterId_AveragingMethod, "Averaging Method", Kernel::ParameterType_UInteger);
|
||||
|
||||
prototype.addOutputParameter(OVP_Algorithm_MatrixVariance_OutputParameterId_AveragedMatrix, "Averaged matrix", Kernel::ParameterType_Matrix);
|
||||
prototype.addOutputParameter(OVP_Algorithm_MatrixVariance_OutputParameterId_Variance, "Matrix variance", Kernel::ParameterType_Matrix);
|
||||
prototype.addOutputParameter(OVP_Algorithm_MatrixVariance_OutputParameterId_ConfidenceBound, "Confidence bound", Kernel::ParameterType_Matrix);
|
||||
|
||||
prototype.addInputTrigger(OVP_Algorithm_MatrixVariance_InputTriggerId_Reset, "Reset");
|
||||
prototype.addInputTrigger(OVP_Algorithm_MatrixVariance_InputTriggerId_FeedMatrix, "Feed matrix");
|
||||
prototype.addInputTrigger(OVP_Algorithm_MatrixVariance_InputTriggerId_ForceAverage, "Force average");
|
||||
|
||||
prototype.addOutputTrigger(OVP_Algorithm_MatrixVariance_OutputTriggerId_AveragePerformed, "Average performed");
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(IAlgorithmDesc, OVP_ClassId_Algorithm_MatrixVarianceDesc)
|
||||
};
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
|
||||
#endif
|
||||
+92
@@ -0,0 +1,92 @@
|
||||
///-------------------------------------------------------------------------------------------------
|
||||
///
|
||||
/// \file windowFunctions.cpp
|
||||
/// \brief Implementation of Windowing functions
|
||||
/// \author Alison Cellard
|
||||
/// \version 1.0
|
||||
/// \date 13/11/2013
|
||||
///
|
||||
/// \copyright (C) 2021 INRIA
|
||||
///
|
||||
/// This program is free software: you can redistribute it and/or modify
|
||||
/// it under the terms of the GNU Affero General Public License as published
|
||||
/// by the Free Software Foundation, either version 3 of the License, or
|
||||
/// (at your option) any later version.
|
||||
///
|
||||
/// This program is distributed in the hope that it will be useful,
|
||||
/// but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
/// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
/// GNU Affero General Public License for more details.
|
||||
///
|
||||
/// You should have received a copy of the GNU Affero General Public License
|
||||
/// along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
///-------------------------------------------------------------------------------------------------
|
||||
#if defined(TARGET_HAS_ThirdPartyEIGEN)
|
||||
|
||||
#include "windowFunctions.hpp"
|
||||
#include <iostream>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
namespace WindowFunctions {
|
||||
|
||||
bool bartlett(Eigen::VectorXd& window, const size_t size)
|
||||
{
|
||||
for (size_t i = 0; i < size; ++i)
|
||||
{
|
||||
if (i <= (size - 1) / 2)
|
||||
{
|
||||
window(i) = 2. * i / (size - 1);
|
||||
}
|
||||
else if (i < size) {
|
||||
window(i) = 2. * ((size - 1) - i) / (size - 1);
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
bool hamming(Eigen::VectorXd& window, const size_t size)
|
||||
{
|
||||
for (size_t i = 0; i < size; ++i)
|
||||
{
|
||||
window(i) = 0.54 - 0.46 * cos(2. * M_PI * i / (size - 1));
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
bool hann(Eigen::VectorXd& window, const size_t size)
|
||||
{
|
||||
for (size_t i = 0; i < size; ++i)
|
||||
{
|
||||
window(i) = 0.5 - 0.5 * cos(2. * M_PI * i / (size - 1));
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
bool parzen(Eigen::VectorXd& window, const size_t size)
|
||||
{
|
||||
for (size_t i = 0; i < size; ++i)
|
||||
{
|
||||
window(i) = 1. - pow((i - (size - 1.) / 2.) / ((size + 1.) / 2.), 2);
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
bool welch(Eigen::VectorXd& window, const size_t size)
|
||||
{
|
||||
for (size_t i = 0; i < size; ++i)
|
||||
{
|
||||
window(i) = 1.0 - fabs((i - (size - 1.0) / 2.0) / ((size + 1.0) / 2.0));
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
} //namespace WindowFunctions
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
|
||||
#endif //TARGET_HAS_ThirdPartyEIGEN
|
||||
+77
@@ -0,0 +1,77 @@
|
||||
///-------------------------------------------------------------------------------------------------
|
||||
///
|
||||
/// \file windowFunctions.hpp
|
||||
/// \brief Windowing functions and helpers for Connectivity Measure
|
||||
/// \author Alison Cellard
|
||||
/// \version 1.0
|
||||
/// \date 13/11/2013
|
||||
///
|
||||
/// \copyright (C) 2021 INRIA
|
||||
///
|
||||
/// This program is free software: you can redistribute it and/or modify
|
||||
/// it under the terms of the GNU Affero General Public License as published
|
||||
/// by the Free Software Foundation, either version 3 of the License, or
|
||||
/// (at your option) any later version.
|
||||
///
|
||||
/// This program is distributed in the hope that it will be useful,
|
||||
/// but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
/// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
/// GNU Affero General Public License for more details.
|
||||
///
|
||||
/// You should have received a copy of the GNU Affero General Public License
|
||||
/// along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
///-------------------------------------------------------------------------------------------------
|
||||
#pragma once
|
||||
|
||||
#if defined(TARGET_HAS_ThirdPartyEIGEN)
|
||||
|
||||
#include <openvibe/ov_all.h>
|
||||
#include <Eigen/Dense>
|
||||
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
namespace WindowFunctions {
|
||||
|
||||
///
|
||||
/// \brief Generate Bartlett window
|
||||
/// \param windowBuffer The buffer in which the window is stored
|
||||
/// \param size The size of the window
|
||||
/// \return True
|
||||
bool bartlett(Eigen::VectorXd& window, const size_t size);
|
||||
|
||||
///
|
||||
/// \brief Generate Hamming window
|
||||
/// \param windowBuffer The buffer in which the window is stored
|
||||
/// \param size The size of the window
|
||||
/// \return True
|
||||
bool hamming(Eigen::VectorXd& window, const size_t size);
|
||||
|
||||
///
|
||||
/// \brief Generate Hann window
|
||||
/// \param windowBuffer The buffer in which the window is stored
|
||||
/// \param size The size of the window
|
||||
/// \return True
|
||||
bool hann(Eigen::VectorXd& window, const size_t size);
|
||||
|
||||
///
|
||||
/// \brief Generate Parzen window
|
||||
/// \param windowBuffer The buffer in which the window is stored
|
||||
/// \param size The size of the window
|
||||
/// \return True
|
||||
bool parzen(Eigen::VectorXd& window, const size_t size);
|
||||
|
||||
///
|
||||
/// \brief Generate Welch window
|
||||
/// \param windowBuffer The buffer in which the window is stored
|
||||
/// \param size The size of the window
|
||||
/// \return True
|
||||
bool welch(Eigen::VectorXd& window, const size_t size);
|
||||
|
||||
} // namespace WindowFunctions
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
|
||||
#endif //TARGET_HAS_ThirdPartyEIGEN
|
||||
+289
@@ -0,0 +1,289 @@
|
||||
///-------------------------------------------------------------------------------------------------
|
||||
///
|
||||
/// \file connectivityMeasureMetrics.cpp
|
||||
/// \brief All connectivity metrics.
|
||||
/// \author Arthur Desbois (Inria).
|
||||
/// \version 1.0
|
||||
/// \date 30/10/2020
|
||||
///
|
||||
/// \copyright (C) 2020 INRIA
|
||||
///
|
||||
/// This program is free software: you can redistribute it and/or modify
|
||||
/// it under the terms of the GNU Affero General Public License as published
|
||||
/// by the Free Software Foundation, either version 3 of the License, or
|
||||
/// (at your option) any later version.
|
||||
///
|
||||
/// This program is distributed in the hope that it will be useful,
|
||||
/// but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
/// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
/// GNU Affero General Public License for more details.
|
||||
///
|
||||
/// You should have received a copy of the GNU Affero General Public License
|
||||
/// along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
///-------------------------------------------------------------------------------------------------
|
||||
|
||||
#include <cmath>
|
||||
#include <complex>
|
||||
#include <iostream>
|
||||
|
||||
|
||||
#include <Eigen/Dense>
|
||||
#include <unsupported/Eigen/FFT>
|
||||
|
||||
#include "connectivityMeasure.hpp"
|
||||
#include <openvibe/ov_all.h>
|
||||
#include <windowFunctions.hpp>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
//************************************************************************
|
||||
//******* Connectivity measurements & associated helper functions ********
|
||||
//************************************************************************
|
||||
|
||||
bool ConnectivityMeasure::initialize(const EConnectMetric metric,
|
||||
EConnectWindowMethod windowMethod,
|
||||
int windowLength,
|
||||
int windowOverlap,
|
||||
size_t nbChannels,
|
||||
size_t fftSize,
|
||||
bool dcRemoval)
|
||||
{
|
||||
m_metric = metric;
|
||||
m_windowMethod = windowMethod;
|
||||
|
||||
m_windowLength = windowLength; // size of one welch window (samples)
|
||||
m_windowOverlap = windowOverlap; // overlap btw windows (%)
|
||||
m_windowOverlapSamples = std::floor(double(m_windowLength * windowOverlap) / 100.0);
|
||||
|
||||
m_nbChannels = nbChannels;
|
||||
m_fftSize = fftSize;
|
||||
m_dcRemoval = dcRemoval;
|
||||
|
||||
m_window = Eigen::VectorXd::Zero(m_windowLength);
|
||||
switch (m_windowMethod)
|
||||
{
|
||||
case EConnectWindowMethod::Hamming:
|
||||
WindowFunctions::hamming(m_window, m_windowLength);
|
||||
break;
|
||||
case EConnectWindowMethod::Hann:
|
||||
WindowFunctions::hann(m_window, m_windowLength);
|
||||
break;
|
||||
case EConnectWindowMethod::Welch:
|
||||
WindowFunctions::welch(m_window, m_windowLength);
|
||||
break;
|
||||
}
|
||||
|
||||
// window normalization constant
|
||||
m_u = 0;
|
||||
for (size_t i = 0; i < size_t(m_window.size()); ++i)
|
||||
{
|
||||
m_u += std::pow(m_window(i), 2);
|
||||
}
|
||||
|
||||
// Instantiate the big spectra & x-spectra arrays
|
||||
m_dft.resize(m_nbChannels); // vector of channels x fftsize
|
||||
m_psd.resize(m_nbChannels); // vector of channels x fftsize
|
||||
m_cpsd.resize(m_nbChannels); // matrix of channels x channels x fftsize
|
||||
for (size_t chan = 0; chan < m_nbChannels; chan++)
|
||||
{
|
||||
m_cpsd[chan].resize(m_nbChannels);
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool ConnectivityMeasure::process(const std::vector <Eigen::VectorXd>& samples,
|
||||
std::vector <Eigen::MatrixXd>& connectivityMatrix)
|
||||
{
|
||||
// Windowing
|
||||
size_t windowShift = m_window.size() - m_windowOverlapSamples;
|
||||
size_t nbWindows = std::floor(double(samples[0].size() - windowShift) / double(windowShift));
|
||||
|
||||
// Remove the DC component of each channel
|
||||
std::vector <Eigen::VectorXd> mySamples;
|
||||
if (m_dcRemoval)
|
||||
{
|
||||
for (size_t chan = 0; chan < m_nbChannels; chan++)
|
||||
{
|
||||
Eigen::VectorXd temp = samples[chan] - Eigen::VectorXd::Ones(samples[chan].size()) * samples[chan].mean();
|
||||
mySamples.push_back(temp);
|
||||
}
|
||||
} else
|
||||
{
|
||||
mySamples = samples;
|
||||
}
|
||||
|
||||
// TODO : don't recompute the whole set of DFTs, because connectivity measures overlap !
|
||||
// Periodigrams (DFTs)
|
||||
for (size_t chan = 0; chan < m_nbChannels; chan++)
|
||||
{
|
||||
periodogram(mySamples[chan], m_dft[chan], nbWindows);
|
||||
}
|
||||
|
||||
// PSDs
|
||||
for (size_t chan = 0; chan < m_nbChannels; chan++)
|
||||
{
|
||||
powerSpectralDensity(m_dft[chan], m_psd[chan], nbWindows);
|
||||
}
|
||||
|
||||
|
||||
// Cross-SPECTRA
|
||||
for (size_t chan1 = 0; chan1 < m_nbChannels; chan1++)
|
||||
{
|
||||
for (size_t chan2 = chan1; chan2 < m_nbChannels; chan2++)
|
||||
{
|
||||
crossSpectralDensity(m_dft[chan1], m_dft[chan2], m_cpsd[chan1][chan2], nbWindows);
|
||||
}
|
||||
}
|
||||
|
||||
// Use PSDs & x-spectra to compute the connectivity matrix
|
||||
switch (m_metric)
|
||||
{
|
||||
case EConnectMetric::Coherence:
|
||||
return coherence(connectivityMatrix);
|
||||
case EConnectMetric::MagnitudeSquaredCoherence:
|
||||
return magnitudeSquaredCoherence(connectivityMatrix);
|
||||
case EConnectMetric::ImaginaryCoherence:
|
||||
return imaginaryCoherence(connectivityMatrix);
|
||||
case EConnectMetric::AbsImaginaryCoherence:
|
||||
return absImaginaryCoherence(connectivityMatrix);
|
||||
default:
|
||||
return coherence(connectivityMatrix);
|
||||
}
|
||||
}
|
||||
|
||||
bool ConnectivityMeasure::periodogram(const Eigen::VectorXd& input,
|
||||
Eigen::MatrixXcd& periodograms,
|
||||
const size_t& nSegments)
|
||||
{
|
||||
periodograms = Eigen::MatrixXcd::Zero(m_fftSize, nSegments);
|
||||
|
||||
// Cut input vector into segments, and apply window to each segment
|
||||
for (size_t k = 0; k < nSegments; ++k)
|
||||
{
|
||||
Eigen::VectorXd segment = Eigen::VectorXd::Zero(m_fftSize);
|
||||
|
||||
for (size_t i = 0; i < size_t(m_windowLength); ++i)
|
||||
{
|
||||
segment(i) = input(i + k * m_windowOverlapSamples) * m_window(i);
|
||||
}
|
||||
|
||||
Eigen::VectorXcd dft = Eigen::VectorXcd::Zero(m_fftSize);
|
||||
|
||||
m_fft.fwd(dft, segment, m_fftSize);
|
||||
periodograms.col(k) = dft;
|
||||
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
bool ConnectivityMeasure::powerSpectralDensity(const Eigen::MatrixXcd& dft,
|
||||
Eigen::VectorXd& output,
|
||||
const size_t& nSegments)
|
||||
{
|
||||
// output(i) will be the power for the band i across segments (time) as summed from the periodogram
|
||||
output = Eigen::VectorXd::Zero(m_fftSize);
|
||||
|
||||
for (size_t k = 0; k < nSegments; ++k)
|
||||
{
|
||||
output += dft.col(k).cwiseAbs2();
|
||||
}
|
||||
double factor = double(nSegments) * m_u;
|
||||
output /= factor;
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
bool ConnectivityMeasure::crossSpectralDensity(const Eigen::MatrixXcd& dft1,
|
||||
const Eigen::MatrixXcd& dft2,
|
||||
Eigen::VectorXcd& output,
|
||||
const size_t& nSegments)
|
||||
{
|
||||
output = Eigen::VectorXcd::Zero(m_fftSize);
|
||||
|
||||
for (size_t k = 0; k < nSegments; ++k)
|
||||
{
|
||||
output += dft2.col(k).cwiseProduct(dft1.col(k).conjugate());
|
||||
}
|
||||
double factor = double(nSegments) * m_u;
|
||||
output /= factor;
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
bool ConnectivityMeasure::coherence(std::vector <Eigen::MatrixXd>& connectivityMatrix)
|
||||
{
|
||||
for (size_t chan1 = 0; chan1 < m_nbChannels; chan1++)
|
||||
{
|
||||
|
||||
connectivityMatrix[chan1].row(chan1) = Eigen::VectorXd::Zero(m_psd[0].size());
|
||||
|
||||
for (size_t chan2 = chan1 + 1; chan2 < m_nbChannels; chan2++)
|
||||
{
|
||||
connectivityMatrix[chan1].row(chan2) = m_cpsd[chan1][chan2].cwiseAbs2().cwiseQuotient(
|
||||
m_psd[chan1].cwiseProduct(m_psd[chan2])).cwiseSqrt();
|
||||
connectivityMatrix[chan2].row(chan1) = connectivityMatrix[chan1].row(chan2);
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
bool ConnectivityMeasure::magnitudeSquaredCoherence(std::vector <Eigen::MatrixXd>& connectivityMatrix)
|
||||
{
|
||||
for (size_t chan1 = 0; chan1 < m_nbChannels; chan1++)
|
||||
{
|
||||
|
||||
connectivityMatrix[chan1].row(chan1) = Eigen::VectorXd::Zero(m_psd[0].size());
|
||||
|
||||
for (size_t chan2 = chan1 + 1; chan2 < m_nbChannels; chan2++)
|
||||
{
|
||||
|
||||
connectivityMatrix[chan1].row(chan2) = m_cpsd[chan1][chan2].cwiseAbs2().cwiseQuotient( m_psd[chan1].cwiseProduct(m_psd[chan2]) );
|
||||
connectivityMatrix[chan2].row(chan1) = connectivityMatrix[chan1].row(chan2);
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
bool ConnectivityMeasure::imaginaryCoherence(std::vector <Eigen::MatrixXd>& connectivityMatrix)
|
||||
{
|
||||
for (size_t chan1 = 0; chan1 < m_nbChannels; chan1++)
|
||||
{
|
||||
|
||||
connectivityMatrix[chan1].row(chan1) = Eigen::VectorXd::Zero(m_psd[0].size());
|
||||
|
||||
for (size_t chan2 = chan1 + 1; chan2 < m_nbChannels; chan2++)
|
||||
{
|
||||
|
||||
connectivityMatrix[chan1].row(chan2) = m_cpsd[chan1][chan2].imag().cwiseQuotient(
|
||||
m_psd[chan1].cwiseProduct(m_psd[chan2]).cwiseSqrt());
|
||||
connectivityMatrix[chan2].row(chan1) = connectivityMatrix[chan1].row(chan2);
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
bool ConnectivityMeasure::absImaginaryCoherence(std::vector <Eigen::MatrixXd>& connectivityMatrix)
|
||||
{
|
||||
for (size_t chan1 = 0; chan1 < m_nbChannels; chan1++)
|
||||
{
|
||||
|
||||
connectivityMatrix[chan1].row(chan1) = Eigen::VectorXd::Zero(m_psd[0].size());
|
||||
|
||||
for (size_t chan2 = chan1 + 1; chan2 < m_nbChannels; chan2++)
|
||||
{
|
||||
|
||||
connectivityMatrix[chan1].row(chan2) = m_cpsd[chan1][chan2].imag().cwiseAbs().cwiseQuotient(
|
||||
m_psd[chan1].cwiseProduct(m_psd[chan2]).cwiseSqrt());
|
||||
connectivityMatrix[chan2].row(chan1) = connectivityMatrix[chan1].row(chan2);
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+263
@@ -0,0 +1,263 @@
|
||||
///-------------------------------------------------------------------------------------------------
|
||||
///
|
||||
/// \file connectivityMeasureMetrics.hpp
|
||||
/// \brief All connectivity metrics.
|
||||
/// \author Arthur Desbois (Inria).
|
||||
/// \version 1.0
|
||||
/// \date 30/10/2020
|
||||
///
|
||||
/// \copyright (C) 2020 INRIA
|
||||
///
|
||||
/// This program is free software: you can redistribute it and/or modify
|
||||
/// it under the terms of the GNU Affero General Public License as published
|
||||
/// by the Free Software Foundation, either version 3 of the License, or
|
||||
/// (at your option) any later version.
|
||||
///
|
||||
/// This program is distributed in the hope that it will be useful,
|
||||
/// but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
/// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
/// GNU Affero General Public License for more details.
|
||||
///
|
||||
/// You should have received a copy of the GNU Affero General Public License
|
||||
/// along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
///-------------------------------------------------------------------------------------------------
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <string>
|
||||
|
||||
#include <Eigen/Dense>
|
||||
#include <unsupported/Eigen/FFT>
|
||||
|
||||
#include <openvibe/ov_all.h>
|
||||
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
/// \brief Enumeration of metrics.
|
||||
enum class EConnectMetric
|
||||
{
|
||||
Coherence,
|
||||
MagnitudeSquaredCoherence,
|
||||
ImaginaryCoherence,
|
||||
AbsImaginaryCoherence
|
||||
};
|
||||
|
||||
/// \brief Convert Metrics to string.
|
||||
/// \param metric The metric.
|
||||
/// \return The metric as human readable string
|
||||
inline std::string toString(const EConnectMetric metric)
|
||||
{
|
||||
switch (metric)
|
||||
{
|
||||
case EConnectMetric::Coherence:
|
||||
return "Coherence";
|
||||
case EConnectMetric::MagnitudeSquaredCoherence:
|
||||
return "MagnitudeSquaredCoherence";
|
||||
case EConnectMetric::ImaginaryCoherence:
|
||||
return "ImaginaryCoherence";
|
||||
case EConnectMetric::AbsImaginaryCoherence:
|
||||
return "AbsImaginaryCoherence";
|
||||
}
|
||||
}
|
||||
|
||||
/// \brief Convert string to Metric.
|
||||
/// \param metric The metric as a string
|
||||
/// \return \ref EConnectMetric
|
||||
inline EConnectMetric StringToMetric(const std::string& metric)
|
||||
{
|
||||
if (metric == "Coherence")
|
||||
{ return EConnectMetric::Coherence; }
|
||||
if (metric == "MagnitudeSquaredCoherence")
|
||||
{ return EConnectMetric::MagnitudeSquaredCoherence; }
|
||||
if (metric == "ImaginaryCoherence")
|
||||
{ return EConnectMetric::ImaginaryCoherence; }
|
||||
if (metric == "AbsImaginaryCoherence")
|
||||
{ return EConnectMetric::AbsImaginaryCoherence; }
|
||||
|
||||
return EConnectMetric::Coherence; // default
|
||||
}
|
||||
|
||||
/// \brief Enumeration of windowing methods
|
||||
enum class EConnectWindowMethod
|
||||
{
|
||||
Hamming,
|
||||
Hann,
|
||||
Welch
|
||||
};
|
||||
|
||||
/// \brief Convert Window method to string.
|
||||
/// \param metric The metric.
|
||||
/// \return the window method as human readable string
|
||||
inline std::string toString(const EConnectWindowMethod winMethod)
|
||||
{
|
||||
switch (winMethod)
|
||||
{
|
||||
case EConnectWindowMethod::Hamming:
|
||||
return "Hamming";
|
||||
case EConnectWindowMethod::Hann:
|
||||
return "Hann";
|
||||
case EConnectWindowMethod::Welch:
|
||||
return "Welch";
|
||||
}
|
||||
}
|
||||
|
||||
class ConnectivityMeasure
|
||||
{
|
||||
public:
|
||||
ConnectivityMeasure()
|
||||
{};
|
||||
|
||||
~ConnectivityMeasure()
|
||||
{};
|
||||
|
||||
bool initialize(const EConnectMetric metric,
|
||||
EConnectWindowMethod windowMethod,
|
||||
int windowLength, int windowOverlap,
|
||||
size_t nbChannels, size_t fftSize,
|
||||
bool dcRemoval);
|
||||
|
||||
/// \brief Select the function to call for the connectivity measurement.
|
||||
/// \param samples The input data set \f$x\f$. With \f$ nChan \f$ channels and \f$ nSamp \f$ samples per channel
|
||||
/// \param connectivityMatrix The connectivity matrix
|
||||
/// \param metric The chosen metric
|
||||
/// \param windowsMethod The windowing method
|
||||
/// \param windowLength The length of one window of processing (eg for Welch)
|
||||
/// \param windowOverlap The overlap btw windows
|
||||
/// \param connectLength The length of one connectivity measurement window
|
||||
/// \param connectOverlap The overlap btw connectivity windows
|
||||
bool process(const std::vector <Eigen::VectorXd>& samples,
|
||||
std::vector <Eigen::MatrixXd>& connectivityMatrix);
|
||||
|
||||
private:
|
||||
/// \brief Calculation of the Coherence
|
||||
/// \f$ Pxx = PSD of x \f$ // \f$ Pyy = PSD of y \f$ // \f$ Sxy = cross spectral density of x and y \f$
|
||||
/// \f$ MSC = \frac{\left| Sxy \right|}{sqrt{(Pxx.Pyy)} } \f$
|
||||
///
|
||||
/// The connectivity matrix is symmetrical, so we only need to compute its top part
|
||||
/// and discard the diagonal (no need to compute connectivity of a single channel...)
|
||||
/// eg [0 1 2 3] :
|
||||
/// [ 01 02 03]
|
||||
/// [ 12 13]
|
||||
/// [ 23]
|
||||
/// [ ]
|
||||
/// \param connectivityMatrix The connectivity matrix as a vector of 2D Matrices of size nbChannels x (nbChannles x frequency_taps)
|
||||
/// \return True if it succeeds, false if it fails.
|
||||
/// \todo don't recompute the whole table, because connectivity measures overlap !
|
||||
bool coherence(std::vector <Eigen::MatrixXd>& connectivityMatrix);
|
||||
|
||||
/// \brief Calculation of the Magnitude Squared Coherence
|
||||
/// \f$ Pxx = PSD of x \f$ // \f$ Pyy = PSD of y \f$ // \f$ Sxy = cross spectral density of x and y \f$
|
||||
/// \f$ MSC = \frac{\left| Sxy \right|^2}{(Pxx.Pyy)} \f$
|
||||
///
|
||||
/// The connectivity matrix is symmetrical, so we only need to compute its top part
|
||||
/// and discard the diagonal (no need to compute connectivity of a single channel...)
|
||||
/// eg [0 1 2 3] :
|
||||
/// [ 01 02 03]
|
||||
/// [ 12 13]
|
||||
/// [ 23]
|
||||
/// [ ]
|
||||
/// \param connectivityMatrix The connectivity matrix as a vector of 2D Matrices of size nbChannels x (nbChannles x frequency_taps)
|
||||
/// \return True if it succeeds, false if it fails.
|
||||
/// \todo don't recompute the whole table, because connectivity measures overlap !
|
||||
bool magnitudeSquaredCoherence(std::vector <Eigen::MatrixXd>& connectivityMatrix);
|
||||
|
||||
/// \brief Calculation of the Imaginary part of the coherence
|
||||
/// \f$ Pxx = PSD of x \f$ // \f$ Pyy = PSD of y \f$ // \f$ Sxy = cross spectral density of x and y \f$
|
||||
/// \f$ ImC = \frac{Im(Sxy)}{sqrt{Pxx.Pyy} } \f$
|
||||
/// The connectivity matrix is symmetrical, so we only need to compute
|
||||
/// its top part, and discard the diagonal (no need to compute connectivity of a single channel...)
|
||||
/// eg [0 1 2 3] :
|
||||
/// [ 01 02 03]
|
||||
/// [ 12 13]
|
||||
/// [ 23]
|
||||
/// [ ]
|
||||
/// \param connectivityMatrix The connectivity matrix as a vector of 2D Matrices of size nbChannels x (nbChannles x frequency_taps)
|
||||
/// \return True if it succeeds, false if it fails.
|
||||
/// \todo don't recompute the whole table, because connectivity measures overlap !
|
||||
bool imaginaryCoherence(std::vector <Eigen::MatrixXd>& connectivityMatrix);
|
||||
|
||||
/// \brief Calculation of the absolute value of the Imaginary part of the coherence
|
||||
/// \f$ Pxx = PSD of x \f$ // \f$ Pyy = PSD of y \f$ // \f$ Sxy = cross spectral density of x and y \f$
|
||||
/// \f$ ImC = \frac{\left| Im(Sxy) \right|}{sqrt{Pxx.Pyy} } \f$
|
||||
/// The connectivity matrix is symmetrical, so we only need to compute
|
||||
/// its top part, and discard the diagonal (no need to compute connectivity of a single channel...)
|
||||
/// eg [0 1 2 3] :
|
||||
/// [ 01 02 03]
|
||||
/// [ 12 13]
|
||||
/// [ 23]
|
||||
/// [ ]
|
||||
/// \param connectivityMatrix The connectivity matrix as a vector of 2D Matrices of size nbChannels x (nbChannles x frequency_taps)
|
||||
/// \return True if it succeeds, false if it fails.
|
||||
/// \todo don't recompute the whole table, because connectivity measures overlap !
|
||||
bool absImaginaryCoherence(std::vector <Eigen::MatrixXd>& connectivityMatrix);
|
||||
|
||||
///
|
||||
/// \brief Generates periodigram of signal
|
||||
/// \param input The signal
|
||||
/// \param periodograms The generated periodigram
|
||||
/// \param nSegments
|
||||
/// \return True on succes, false otherwise
|
||||
bool periodogram(const Eigen::VectorXd& input,
|
||||
Eigen::MatrixXcd& periodograms,
|
||||
const size_t& nSegments);
|
||||
|
||||
///
|
||||
/// \brief Computes the spectral density of a spectrum
|
||||
/// \param dft The discrete fourier transform
|
||||
/// \param output The generated spectral density
|
||||
/// \param nSegments
|
||||
/// \return True on success, false otherwise
|
||||
bool powerSpectralDensity(const Eigen::MatrixXcd& dft,
|
||||
Eigen::VectorXd& output,
|
||||
const size_t& nSegments);
|
||||
|
||||
///
|
||||
/// \brief Computes cross spectral density of 2 spectra
|
||||
/// Only computes the top diagonal of the CPSD matrix
|
||||
/// eg [0 1 2 3] :
|
||||
/// [00 01 02 03]
|
||||
/// [ 11 12 13]
|
||||
/// [ 22 23]
|
||||
/// [ 33]
|
||||
/// \param dft1 The first discrete fourier transform
|
||||
/// \param dft2 The second discrete fourier transform
|
||||
/// \param output The generated cross spectral density matrix
|
||||
/// \param nSegments
|
||||
/// \return True on success, false otherwise
|
||||
///
|
||||
/// \todo : don't compute only the top part, because some connectivity measurements are not symmetrical ?
|
||||
bool crossSpectralDensity(const Eigen::MatrixXcd& dft1,
|
||||
const Eigen::MatrixXcd& dft2,
|
||||
Eigen::VectorXcd& output,
|
||||
const size_t& nSegments);
|
||||
|
||||
// Parameters / Members
|
||||
Eigen::FFT<double, Eigen::internal::kissfft_impl<double>> m_fft; // Instance of the fft transform
|
||||
|
||||
EConnectMetric m_metric = EConnectMetric::Coherence;
|
||||
EConnectWindowMethod m_windowMethod = EConnectWindowMethod::Hann;
|
||||
Eigen::VectorXd m_window; // Window used for Welch method
|
||||
double m_u = 0; // Window normalization factor
|
||||
|
||||
std::vector <Eigen::MatrixXcd> m_dft; // Discrete Fourier Transform
|
||||
std::vector <Eigen::VectorXd> m_psd; // Power Spectral Density
|
||||
std::vector <std::vector<Eigen::VectorXcd>> m_cpsd; // Cross Power Spectral Density
|
||||
|
||||
int m_windowLength = 128; // size of one Welch window (samples)
|
||||
int m_windowOverlap = 50; // overlap btw windows (%)
|
||||
int m_windowOverlapSamples = 64;
|
||||
int m_nbWindows = 8;
|
||||
|
||||
size_t m_fftSize = 256;
|
||||
size_t m_nbChannels = 1;
|
||||
|
||||
bool m_dcRemoval = false;
|
||||
|
||||
};
|
||||
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+168
@@ -0,0 +1,168 @@
|
||||
///-------------------------------------------------------------------------------------------------
|
||||
///
|
||||
/// \file CBoxAlgorithmMatrix3dTo2d.cpp
|
||||
/// \brief Implementation of the box Matrix3dTo2d
|
||||
/// \author Arthur DESBOIS (INRIA).
|
||||
/// \version 0.0.1.
|
||||
/// \date Fri Feb 12 15:13:00 2021.
|
||||
///
|
||||
/// \copyright (C) 2021 INRIA
|
||||
///
|
||||
/// This program is free software: you can redistribute it and/or modify
|
||||
/// it under the terms of the GNU Affero General Public License as published
|
||||
/// by the Free Software Foundation, either version 3 of the License, or
|
||||
/// (at your option) any later version.
|
||||
///
|
||||
/// This program is distributed in the hope that it will be useful,
|
||||
/// but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
/// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
/// GNU Affero General Public License for more details.
|
||||
///
|
||||
/// You should have received a copy of the GNU Affero General Public License
|
||||
/// along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
///-------------------------------------------------------------------------------------------------
|
||||
|
||||
#include "CBoxAlgorithmMatrix3dTo2d.hpp"
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
bool CBoxAlgorithmMatrix3dTo2d::initialize()
|
||||
{
|
||||
m_matrixDecoder.initialize(*this, 0);
|
||||
m_matrixEncoder.initialize(*this, 0);
|
||||
|
||||
m_iMatrix = m_matrixDecoder.getOutputMatrix();
|
||||
m_oMatrix = m_matrixEncoder.getInputMatrix();
|
||||
|
||||
m_dimensionToRemove = int(FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 0));
|
||||
m_removedDimensionIdx = int(FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 1));
|
||||
|
||||
OV_ERROR_UNLESS_KRF(m_dimensionToRemove == 0 || m_dimensionToRemove == 1 || m_dimensionToRemove == 2, "Invalid dimension number", Kernel::ErrorType::BadInput);
|
||||
|
||||
return true;
|
||||
}
|
||||
/*******************************************************************************/
|
||||
|
||||
bool CBoxAlgorithmMatrix3dTo2d::uninitialize()
|
||||
{
|
||||
m_matrixDecoder.uninitialize();
|
||||
m_matrixEncoder.uninitialize();
|
||||
|
||||
return true;
|
||||
}
|
||||
/*******************************************************************************/
|
||||
|
||||
|
||||
bool CBoxAlgorithmMatrix3dTo2d::processInput(const size_t index)
|
||||
{
|
||||
getBoxAlgorithmContext()->markAlgorithmAsReadyToProcess();
|
||||
return true;
|
||||
}
|
||||
|
||||
/*******************************************************************************/
|
||||
|
||||
|
||||
bool CBoxAlgorithmMatrix3dTo2d::process()
|
||||
{
|
||||
const Kernel::IBox& staticBoxContext=this->getStaticBoxContext();
|
||||
Kernel::IBoxIO& boxContext = this->getDynamicBoxContext();
|
||||
|
||||
for (size_t i = 0; i < boxContext.getInputChunkCount(0); ++i)
|
||||
{
|
||||
m_matrixDecoder.decode(i);
|
||||
|
||||
if(m_matrixDecoder.isHeaderReceived())
|
||||
{
|
||||
OV_ERROR_UNLESS_KRF(m_iMatrix->getDimensionCount() == 3, "Input matrix should have 3 dimensions", Kernel::ErrorType::BadInput);
|
||||
|
||||
m_dim0Size = m_iMatrix->getDimensionSize(0);
|
||||
m_dim1Size = m_iMatrix->getDimensionSize(1);
|
||||
m_dim2Size = m_iMatrix->getDimensionSize(2);
|
||||
|
||||
switch(m_dimensionToRemove) {
|
||||
case 0:
|
||||
OV_ERROR_UNLESS_KRF(m_dim0Size > m_removedDimensionIdx, "Idx in removed dimension over dimension size", Kernel::ErrorType::BadInput);
|
||||
MatrixInit(*m_oMatrix, m_dim1Size, m_dim2Size);
|
||||
break;
|
||||
case 1:
|
||||
OV_ERROR_UNLESS_KRF(m_dim1Size > m_removedDimensionIdx, "Idx in removed dimension over dimension size", Kernel::ErrorType::BadInput);
|
||||
MatrixInit(*m_oMatrix, m_dim0Size, m_dim2Size);
|
||||
break;
|
||||
case 2:
|
||||
OV_ERROR_UNLESS_KRF(m_dim2Size > m_removedDimensionIdx, "Idx in removed dimension over dimension size", Kernel::ErrorType::BadInput);
|
||||
MatrixInit(*m_oMatrix, m_dim0Size, m_dim1Size);
|
||||
break;
|
||||
}
|
||||
|
||||
m_matrixEncoder.encodeHeader();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
if(m_matrixDecoder.isBufferReceived())
|
||||
{
|
||||
RemoveDimension(*m_iMatrix, *m_oMatrix);
|
||||
|
||||
this->getLogManager() << Kernel::LogLevel_Debug<< "Received matrix with dimensions " << m_iMatrix->getDimensionSize(0) << " x " << m_iMatrix->getDimensionSize(1) << " x " << m_iMatrix->getDimensionSize(2) << "\n";
|
||||
this->getLogManager() << Kernel::LogLevel_Debug << "Output matrix has dimensions " << m_oMatrix->getDimensionSize(0) << " x " << m_oMatrix->getDimensionSize(1) << "\n";
|
||||
|
||||
m_matrixEncoder.encodeBuffer();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
|
||||
}
|
||||
if(m_matrixDecoder.isEndReceived())
|
||||
{
|
||||
m_matrixEncoder.encodeEnd();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmMatrix3dTo2d::MatrixInit(IMatrix& out, const size_t dim0, const size_t dim1)
|
||||
{
|
||||
out.setDimensionCount(2);
|
||||
out.setDimensionSize(0, dim0);
|
||||
out.setDimensionSize(1, dim1);
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmMatrix3dTo2d::RemoveDimension(const IMatrix& in, IMatrix& out)
|
||||
{
|
||||
size_t idxOutBuffer = 0;
|
||||
|
||||
const double* inBuffer = in.getBuffer();
|
||||
double* outBuffer = out.getBuffer();
|
||||
|
||||
switch(m_dimensionToRemove) {
|
||||
case 0:
|
||||
for(size_t idx1 = 0; idx1 < m_dim1Size; idx1++) {
|
||||
for(size_t idx2 = 0; idx2 < m_dim2Size; idx2++) {
|
||||
outBuffer[idxOutBuffer++] = inBuffer[m_removedDimensionIdx*m_dim1Size*m_dim2Size + idx1*m_dim2Size + idx2];
|
||||
}
|
||||
}
|
||||
break;
|
||||
case 1:
|
||||
for(size_t idx0 = 0; idx0 < m_dim0Size; idx0++) {
|
||||
for(size_t idx2 = 0; idx2 < m_dim2Size; idx2++) {
|
||||
outBuffer[idxOutBuffer++] = inBuffer[idx0*m_dim1Size*m_dim2Size + m_removedDimensionIdx*m_dim2Size + idx2];
|
||||
}
|
||||
}
|
||||
break;
|
||||
case 2:
|
||||
for(size_t idx0 = 0; idx0 < m_dim0Size; idx0++) {
|
||||
for(size_t idx1 = 0; idx1 < m_dim1Size; idx1++) {
|
||||
outBuffer[idxOutBuffer++] = inBuffer[idx0*m_dim1Size*m_dim2Size + idx1*m_dim2Size + m_removedDimensionIdx];
|
||||
}
|
||||
}
|
||||
break;
|
||||
}
|
||||
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+114
@@ -0,0 +1,114 @@
|
||||
///-------------------------------------------------------------------------------------------------
|
||||
///
|
||||
/// \file CBoxAlgorithmMatrix3dTo2d.hpp
|
||||
/// \brief Classes of the box Matrix3dTo2d
|
||||
/// \author Arthur DESBOIS (INRIA).
|
||||
/// \version 0.0.1.
|
||||
/// \date Fri Feb 12 15:13:00 2021.
|
||||
///
|
||||
/// \copyright (C) 2021 INRIA
|
||||
///
|
||||
/// This program is free software: you can redistribute it and/or modify
|
||||
/// it under the terms of the GNU Affero General Public License as published
|
||||
/// by the Free Software Foundation, either version 3 of the License, or
|
||||
/// (at your option) any later version.
|
||||
///
|
||||
/// This program is distributed in the hope that it will be useful,
|
||||
/// but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
/// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
/// GNU Affero General Public License for more details.
|
||||
///
|
||||
/// You should have received a copy of the GNU Affero General Public License
|
||||
/// along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
///-------------------------------------------------------------------------------------------------
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ovp_defines.h"
|
||||
|
||||
#include <openvibe/ov_all.h>
|
||||
#include <toolkit/ovtk_all.h>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
/// \brief The class CBoxAlgorithmMatrix3dTo2d describes the box Matrix 3D to 2D.
|
||||
class CBoxAlgorithmMatrix3dTo2d final : virtual public Toolkit::TBoxAlgorithm<IBoxAlgorithm>
|
||||
{
|
||||
public:
|
||||
void release() override { delete this; }
|
||||
|
||||
bool initialize() override;
|
||||
|
||||
bool uninitialize() override;
|
||||
|
||||
bool processInput(const size_t index) override;
|
||||
|
||||
bool process() override;
|
||||
|
||||
_IsDerivedFromClass_Final_(Toolkit::TBoxAlgorithm<IBoxAlgorithm>, OVP_ClassId_BoxAlgorithm_Matrix3dTo2d)
|
||||
|
||||
protected:
|
||||
// Codecs
|
||||
Toolkit::TStreamedMatrixDecoder <CBoxAlgorithmMatrix3dTo2d> m_matrixDecoder;
|
||||
Toolkit::TStreamedMatrixEncoder <CBoxAlgorithmMatrix3dTo2d> m_matrixEncoder;
|
||||
|
||||
// Matrices
|
||||
CMatrix* m_iMatrix = nullptr;
|
||||
CMatrix* m_oMatrix = nullptr;
|
||||
|
||||
// Parameters
|
||||
int m_dimensionToRemove;
|
||||
int m_removedDimensionIdx;
|
||||
|
||||
size_t m_dim0Size;
|
||||
size_t m_dim1Size;
|
||||
size_t m_dim2Size;
|
||||
|
||||
private:
|
||||
bool MatrixInit(CMatrix& m, const size_t dim0, const size_t dim1);
|
||||
|
||||
bool RemoveDimension(const CMatrix& in, CMatrix& out);
|
||||
|
||||
};
|
||||
|
||||
|
||||
/// \brief Descriptor of the box Matrix 3D to 2D.
|
||||
class CBoxAlgorithmMatrix3dTo2dDesc final : virtual public IBoxAlgorithmDesc
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override
|
||||
{}
|
||||
|
||||
CString getName() const override { return CString("Matrix 3D to 2D"); }
|
||||
CString getAuthorName() const override { return CString("Arthur Desbois"); }
|
||||
CString getAuthorCompanyName() const override { return CString("INRIA"); }
|
||||
CString getShortDescription() const override { return CString("Convert 3D matrices to 2D"); }
|
||||
CString getDetailedDescription() const override { return CString("Convert 3 dimensional matrices to 2D matrices, by selecting a dimension to remove"); }
|
||||
CString getCategory() const override { return CString("Signal processing/Basic"); }
|
||||
CString getVersion() const override { return CString("0.0.1"); }
|
||||
CString getStockItemName() const override { return CString(""); }
|
||||
CIdentifier getCreatedClass() const override { return OVP_ClassId_BoxAlgorithm_Matrix3dTo2d; }
|
||||
IPluginObject* create() override { return new CBoxAlgorithmMatrix3dTo2d; }
|
||||
|
||||
bool getBoxPrototype(Kernel::IBoxProto& prototype) const override
|
||||
{
|
||||
prototype.addInput("input", OV_TypeId_StreamedMatrix);
|
||||
prototype.addOutput("output", OV_TypeId_StreamedMatrix);
|
||||
|
||||
prototype.addSetting("Dimension to remove", OV_TypeId_Integer, "");
|
||||
prototype.addSetting("Index in removed dimension", OV_TypeId_Integer, "");
|
||||
|
||||
prototype.addFlag(OV_AttributeId_Box_FlagIsUnstable);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(IBoxAlgorithmDesc, OVP_ClassId_BoxAlgorithm_Matrix3dTo2dDesc)
|
||||
};
|
||||
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+125
@@ -0,0 +1,125 @@
|
||||
#if defined(TARGET_HAS_ThirdPartyEIGEN)
|
||||
|
||||
#include "ovpCBoxAlgorithmARCoefficients.h"
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
bool CBoxAlgorithmARCoefficients::initialize()
|
||||
{
|
||||
// Signal stream decoder
|
||||
m_decoder.initialize(*this, 0);
|
||||
|
||||
m_method = &this->getAlgorithmManager().getAlgorithm(this->getAlgorithmManager().createAlgorithm(OVP_ClassId_Algorithm_ARBurgMethod));
|
||||
m_method->initialize();
|
||||
|
||||
ip_matrix.initialize(m_method->getInputParameter(OVP_Algorithm_ARBurgMethod_InputParameterId_Matrix));
|
||||
op_matrix.initialize(m_method->getOutputParameter(OVP_Algorithm_ARBurgMethod_OutputParameterId_Matrix));
|
||||
|
||||
ip_order.initialize(m_method->getInputParameter(OVP_Algorithm_ARBurgMethod_InputParameterId_UInteger));
|
||||
ip_order = uint64_t(FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 0));
|
||||
|
||||
// Feature vector stream encoder
|
||||
m_encoder.initialize(*this, 0);
|
||||
|
||||
// The AR Burg's Method algorithm will take the matrix coming from the signal decoder:
|
||||
ip_matrix.setReferenceTarget(m_decoder.getOutputMatrix());
|
||||
|
||||
// The feature vector encoder will take the matrix from the AR Burg's Method algorithm:
|
||||
m_encoder.getInputMatrix().setReferenceTarget(op_matrix);
|
||||
|
||||
|
||||
return true;
|
||||
}
|
||||
/*******************************************************************************/
|
||||
|
||||
bool CBoxAlgorithmARCoefficients::uninitialize()
|
||||
{
|
||||
m_decoder.uninitialize();
|
||||
|
||||
|
||||
ip_matrix.uninitialize();
|
||||
ip_order.uninitialize();
|
||||
op_matrix.uninitialize();
|
||||
|
||||
m_method->uninitialize();
|
||||
this->getAlgorithmManager().releaseAlgorithm(*m_method);
|
||||
|
||||
m_encoder.uninitialize();
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
/*******************************************************************************/
|
||||
|
||||
|
||||
bool CBoxAlgorithmARCoefficients::processInput(const size_t /*index*/)
|
||||
{
|
||||
// ready to process !
|
||||
getBoxAlgorithmContext()->markAlgorithmAsReadyToProcess();
|
||||
|
||||
return true;
|
||||
}
|
||||
/*******************************************************************************/
|
||||
|
||||
bool CBoxAlgorithmARCoefficients::process()
|
||||
{
|
||||
// the dynamic box context describes the current state of the box inputs and outputs (i.e. the chunks)
|
||||
Kernel::IBoxIO& boxContext = this->getDynamicBoxContext();
|
||||
|
||||
// we decode the input signal chunks
|
||||
for (size_t i = 0; i < boxContext.getInputChunkCount(0); ++i)
|
||||
{
|
||||
m_decoder.decode(i);
|
||||
|
||||
if (m_decoder.isHeaderReceived())
|
||||
{
|
||||
// Header received
|
||||
m_method->process(OVP_Algorithm_ARBurgMethod_InputTriggerId_Initialize);
|
||||
|
||||
// Make sure the algo initialization was successful
|
||||
if (!m_method->process(OVP_Algorithm_ARBurgMethod_InputTriggerId_Initialize))
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "Initialization was unsuccessful\n";
|
||||
return false;
|
||||
}
|
||||
|
||||
// Pass the header to the next boxes, by encoding a header on the output 0:
|
||||
m_encoder.encodeHeader();
|
||||
|
||||
// send the output chunk containing the header. The dates are the same as the input chunk:
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
|
||||
|
||||
if (m_decoder.isBufferReceived())
|
||||
{
|
||||
// we process the signal matrix with our algorithm
|
||||
m_method->process(OVP_Algorithm_ARBurgMethod_InputTriggerId_Process);
|
||||
|
||||
// If the process is done successfully, we can encode the buffer
|
||||
if (m_method->isOutputTriggerActive(OVP_Algorithm_ARBurgMethod_OutputTriggerId_ProcessDone))
|
||||
{
|
||||
// Encode the output buffer :
|
||||
m_encoder.encodeBuffer();
|
||||
|
||||
// and send it to the next boxes :
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
}
|
||||
if (m_decoder.isEndReceived())
|
||||
{
|
||||
// End of stream received. This happens only once when pressing "stop". Just pass it to the next boxes so they receive the message :
|
||||
m_encoder.encodeEnd();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
#endif // #if defined(TARGET_HAS_ThirdPartyEIGEN)
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+100
@@ -0,0 +1,100 @@
|
||||
#pragma once
|
||||
|
||||
#if defined(TARGET_HAS_ThirdPartyEIGEN)
|
||||
|
||||
//You may have to change this path to match your folder organisation
|
||||
#include "../../ovp_defines.h"
|
||||
|
||||
#include <openvibe/ov_all.h>
|
||||
#include <toolkit/ovtk_all.h>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
/**
|
||||
* \class CBoxAlgorithmARCoefficients
|
||||
* \author Alison Cellard (Inria)
|
||||
* \date Wed Nov 28 10:40:52 2012
|
||||
* \brief The class CBoxAlgorithmARCoefficients describes the box AR Features.
|
||||
*
|
||||
*/
|
||||
class CBoxAlgorithmARCoefficients final : virtual public Toolkit::TBoxAlgorithm<IBoxAlgorithm>
|
||||
{
|
||||
public:
|
||||
void release() override { delete this; }
|
||||
|
||||
bool initialize() override;
|
||||
bool uninitialize() override;
|
||||
|
||||
// Process callbacks on new input received
|
||||
bool processInput(const size_t index) override;
|
||||
|
||||
|
||||
bool process() override;
|
||||
|
||||
// As we do with any class in openvibe, we use the macro below
|
||||
// to associate this box to an unique identifier.
|
||||
// The inheritance information is also made available,
|
||||
// as we provide the superclass Toolkit::TBoxAlgorithm < IBoxAlgorithm >
|
||||
_IsDerivedFromClass_Final_(Toolkit::TBoxAlgorithm<IBoxAlgorithm>, OVP_ClassId_BoxAlgorithm_ARCoefficients)
|
||||
|
||||
protected:
|
||||
|
||||
// Signal stream decoder
|
||||
Toolkit::TSignalDecoder<CBoxAlgorithmARCoefficients> m_decoder;
|
||||
// Feature vector stream encoder
|
||||
Toolkit::TStreamedMatrixEncoder<CBoxAlgorithmARCoefficients> m_encoder;
|
||||
|
||||
Kernel::IAlgorithmProxy* m_method = nullptr;
|
||||
Kernel::TParameterHandler<CMatrix*> ip_matrix;
|
||||
Kernel::TParameterHandler<CMatrix*> op_matrix;
|
||||
Kernel::TParameterHandler<uint64_t> ip_order;
|
||||
};
|
||||
|
||||
|
||||
/**
|
||||
* \class CBoxAlgorithmARCoefficientsDesc
|
||||
* \author Alison Cellard (Inria)
|
||||
* \date Wed Nov 28 10:40:52 2012
|
||||
* \brief Descriptor of the box AR Features.
|
||||
*
|
||||
*/
|
||||
class CBoxAlgorithmARCoefficientsDesc final : virtual public IBoxAlgorithmDesc
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { }
|
||||
|
||||
CString getName() const override { return CString("AutoRegressive Coefficients"); }
|
||||
CString getAuthorName() const override { return CString("Alison Cellard"); }
|
||||
CString getAuthorCompanyName() const override { return CString("Inria"); }
|
||||
|
||||
CString getShortDescription() const override { return CString("Estimates autoregressive (AR) coefficients from a set of signals"); }
|
||||
|
||||
CString getDetailedDescription() const override { return CString("Estimates autoregressive (AR) linear model coefficients using Burg's method"); }
|
||||
|
||||
CString getCategory() const override { return CString("Signal processing/Basic"); }
|
||||
CString getVersion() const override { return CString("1.0"); }
|
||||
CString getStockItemName() const override { return CString("gtk-convert"); }
|
||||
|
||||
CIdentifier getCreatedClass() const override { return OVP_ClassId_BoxAlgorithm_ARCoefficients; }
|
||||
IPluginObject* create() override { return new CBoxAlgorithmARCoefficients; }
|
||||
|
||||
bool getBoxPrototype(Kernel::IBoxProto& prototype) const override
|
||||
{
|
||||
prototype.addInput("EEG Signal",OV_TypeId_Signal);
|
||||
prototype.addOutput("AR Features",OV_TypeId_StreamedMatrix);
|
||||
prototype.addSetting("Order",OV_TypeId_Integer, "1");
|
||||
|
||||
prototype.addFlag(OV_AttributeId_Box_FlagIsUnstable);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(IBoxAlgorithmDesc, OVP_ClassId_BoxAlgorithm_ARCoefficientsDesc)
|
||||
};
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
|
||||
#endif // TARGET_HAS_ThirdPartyEIGEN
|
||||
+141
@@ -0,0 +1,141 @@
|
||||
#include "ovpCBoxAlgorithmDifferentialIntegral.h"
|
||||
|
||||
#include <iostream>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
bool CBoxAlgorithmDifferentialIntegral::initialize()
|
||||
{
|
||||
// Signal stream decoder
|
||||
m_decoder.initialize(*this, 0);
|
||||
// Signal stream encoder
|
||||
m_encoder.initialize(*this, 0);
|
||||
|
||||
// If you need to, you can manually set the reference targets to link the codecs input and output. To do so, you can use :
|
||||
m_encoder.getInputMatrix().setReferenceTarget(m_decoder.getOutputMatrix());
|
||||
m_encoder.getInputSamplingRate().setReferenceTarget(m_decoder.getOutputSamplingRate());
|
||||
|
||||
m_operation = EDifferentialIntegralOperation(uint64_t(FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 0)));
|
||||
m_filterOrder = uint64_t(FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 1));
|
||||
|
||||
return true;
|
||||
}
|
||||
/*******************************************************************************/
|
||||
|
||||
bool CBoxAlgorithmDifferentialIntegral::uninitialize()
|
||||
{
|
||||
m_decoder.uninitialize();
|
||||
m_encoder.uninitialize();
|
||||
|
||||
return true;
|
||||
}
|
||||
/*******************************************************************************/
|
||||
|
||||
bool CBoxAlgorithmDifferentialIntegral::processInput(const size_t /*index*/)
|
||||
{
|
||||
getBoxAlgorithmContext()->markAlgorithmAsReadyToProcess();
|
||||
return true;
|
||||
}
|
||||
/*******************************************************************************/
|
||||
|
||||
double CBoxAlgorithmDifferentialIntegral::operation(const double a, const double b) const
|
||||
{
|
||||
if (m_operation == EDifferentialIntegralOperation::Differential) { return a - b; }
|
||||
if (m_operation == EDifferentialIntegralOperation::Integral) { return a + b; }
|
||||
return 0;
|
||||
}
|
||||
|
||||
|
||||
bool CBoxAlgorithmDifferentialIntegral::process()
|
||||
{
|
||||
Kernel::IBoxIO& boxContext = this->getDynamicBoxContext();
|
||||
|
||||
//iterate over all chunk on input 0
|
||||
for (size_t i = 0; i < boxContext.getInputChunkCount(0); ++i)
|
||||
{
|
||||
// decode the chunk i on input 0
|
||||
m_decoder.decode(i);
|
||||
|
||||
if (m_decoder.isHeaderReceived())
|
||||
{
|
||||
// initialize the past data array
|
||||
CMatrix* matrix = m_decoder.getOutputMatrix(); // the StreamedMatrix of samples.
|
||||
|
||||
|
||||
// initialize all of the tables according to the number of channels
|
||||
m_pastData = new double*[matrix->getDimensionSize(0)];
|
||||
m_tmpData = new double*[matrix->getDimensionSize(0)];
|
||||
m_stabilized = new bool[matrix->getDimensionSize(0)];
|
||||
m_step = new size_t[matrix->getDimensionSize(0)];
|
||||
|
||||
for (size_t k = 0; k < matrix->getDimensionSize(0); ++k)
|
||||
{
|
||||
m_stabilized[k] = false;
|
||||
m_step[k] = 0;
|
||||
m_pastData[k] = new double[m_filterOrder];
|
||||
m_tmpData[k] = new double[m_filterOrder];
|
||||
m_tmpData[k][0] = 0;
|
||||
}
|
||||
|
||||
// Encode the output header
|
||||
m_encoder.encodeHeader();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
|
||||
if (m_decoder.isBufferReceived())
|
||||
{
|
||||
CMatrix* matrix = m_decoder.getOutputMatrix(); // the StreamedMatrix of samples.
|
||||
|
||||
const size_t nChannel = matrix->getDimensionSize(0);
|
||||
const size_t samplesPerChannel = matrix->getDimensionSize(1);
|
||||
|
||||
// ... do some process on the matrix ...
|
||||
|
||||
double* buffer = matrix->getBuffer();
|
||||
|
||||
for (size_t c = 0; c < nChannel; ++c)
|
||||
{
|
||||
for (size_t s = 0; s < samplesPerChannel; ++s)
|
||||
{
|
||||
// save the results of the previous step in a temporary array
|
||||
for (size_t step = 0; step < m_step[c]; ++step) { m_tmpData[c][step] = m_pastData[c][step]; }
|
||||
|
||||
// save the current sample as f^0(x)
|
||||
m_pastData[c][0] = buffer[s + c * samplesPerChannel];
|
||||
|
||||
// save all of the f^n(x)
|
||||
for (size_t step = 1; step < m_step[c]; ++step) { m_pastData[c][step] = operation(m_pastData[c][step - 1], m_tmpData[c][step - 1]); }
|
||||
|
||||
// if the filter is not yet stabilized we increase the step and use 0 as a return value
|
||||
if (!m_stabilized[c])
|
||||
{
|
||||
if (m_step[c] == m_filterOrder) { m_stabilized[c] = true; }
|
||||
else { m_step[c]++; }
|
||||
|
||||
buffer[s + c * samplesPerChannel] = 0;
|
||||
}
|
||||
// otherwise use f^order(x)
|
||||
else { buffer[s + c * samplesPerChannel] = operation(m_pastData[c][m_filterOrder - 1], m_tmpData[c][m_filterOrder - 1]); }
|
||||
}
|
||||
}
|
||||
|
||||
// Encode the output buffer
|
||||
m_encoder.encodeBuffer();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
|
||||
if (m_decoder.isEndReceived())
|
||||
{
|
||||
// End of stream received. This happens only once when pressing "stop". Just pass it to the next boxes so they receive the message :
|
||||
m_encoder.encodeEnd();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+100
@@ -0,0 +1,100 @@
|
||||
#pragma once
|
||||
|
||||
//You may have to change this path to match your folder organisation
|
||||
#include "../../ovp_defines.h"
|
||||
|
||||
#include <openvibe/ov_all.h>
|
||||
#include <toolkit/ovtk_all.h>
|
||||
|
||||
// The unique identifiers for the box and its descriptor.
|
||||
// Identifier are randomly chosen by the skeleton-generator.
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
/**
|
||||
* \class CBoxAlgorithmDifferentialIntegral
|
||||
* \author Jozef Legeny (INRIA)
|
||||
* \date Thu Oct 27 15:24:05 2011
|
||||
* \brief The class CBoxAlgorithmDifferentialIntegral describes the box DifferentialIntegral.
|
||||
*
|
||||
*/
|
||||
class CBoxAlgorithmDifferentialIntegral final : virtual public Toolkit::TBoxAlgorithm<IBoxAlgorithm>
|
||||
{
|
||||
public:
|
||||
void release() override { delete this; }
|
||||
|
||||
bool initialize() override;
|
||||
bool uninitialize() override;
|
||||
|
||||
bool processInput(const size_t index) override;
|
||||
bool process() override;
|
||||
|
||||
_IsDerivedFromClass_Final_(Toolkit::TBoxAlgorithm<IBoxAlgorithm>, OVP_ClassId_BoxAlgorithm_DifferentialIntegral)
|
||||
|
||||
protected:
|
||||
Toolkit::TSignalDecoder<CBoxAlgorithmDifferentialIntegral> m_decoder;
|
||||
Toolkit::TSignalEncoder<CBoxAlgorithmDifferentialIntegral> m_encoder;
|
||||
|
||||
private:
|
||||
double operation(const double a, const double b) const;
|
||||
EDifferentialIntegralOperation m_operation = EDifferentialIntegralOperation::Differential;
|
||||
uint64_t m_filterOrder = 0;
|
||||
|
||||
/// Holds the differentials/integrals of all orders from the previous step
|
||||
double** m_pastData = nullptr;
|
||||
double** m_tmpData = nullptr;
|
||||
|
||||
/// Is true when the filter is stabilized
|
||||
bool* m_stabilized = nullptr;
|
||||
/// Counts the samples up to the filter order, used to stabilize the filter
|
||||
size_t* m_step = nullptr;
|
||||
};
|
||||
|
||||
|
||||
/**
|
||||
* \class CBoxAlgorithmDifferentialIntegralDesc
|
||||
* \author Jozef Legeny (INRIA)
|
||||
* \date Thu Oct 27 15:24:05 2011
|
||||
* \brief Descriptor of the box DifferentialIntegral.
|
||||
*
|
||||
*/
|
||||
class CBoxAlgorithmDifferentialIntegralDesc final : virtual public IBoxAlgorithmDesc
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { }
|
||||
|
||||
CString getName() const override { return CString("Signal Differential/Integral"); }
|
||||
CString getAuthorName() const override { return CString("Jozef Legeny"); }
|
||||
CString getAuthorCompanyName() const override { return CString("INRIA"); }
|
||||
CString getShortDescription() const override { return CString("Calculates a differential or an integral of a signal"); }
|
||||
CString getDetailedDescription() const override { return CString("Calculates a differential or an integral of a signal."); }
|
||||
CString getCategory() const override { return CString("Signal processing/Basic"); }
|
||||
CString getVersion() const override { return CString("1.0"); }
|
||||
CString getStockItemName() const override { return CString("gtk-execute"); }
|
||||
|
||||
CIdentifier getCreatedClass() const override { return OVP_ClassId_BoxAlgorithm_DifferentialIntegral; }
|
||||
IPluginObject* create() override { return new CBoxAlgorithmDifferentialIntegral; }
|
||||
|
||||
/*
|
||||
virtual IBoxListener* createBoxListener() const { return new CBoxAlgorithmDifferentialIntegralListener; }
|
||||
virtual void releaseBoxListener(IBoxListener* listener) const { delete listener; }
|
||||
*/
|
||||
bool getBoxPrototype(Kernel::IBoxProto& prototype) const override
|
||||
{
|
||||
prototype.addInput("Input Signal",OV_TypeId_Signal);
|
||||
|
||||
prototype.addOutput("Output Signal",OV_TypeId_Signal);
|
||||
|
||||
prototype.addSetting("Operation", OVP_TypeId_DifferentialIntegralOperation, "Differential");
|
||||
prototype.addSetting("Order", OV_TypeId_Integer, "1");
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(IBoxAlgorithmDesc, OVP_ClassId_BoxAlgorithm_DifferentialIntegralDesc)
|
||||
};
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+143
@@ -0,0 +1,143 @@
|
||||
#include "ovpCBoxAlgorithmERSPAverage.h"
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
bool CBoxAlgorithmERSPAverage::initialize()
|
||||
{
|
||||
m_epochingStim = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 0);
|
||||
m_computeStim = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 1);
|
||||
|
||||
m_decoderSpectrum.initialize(*this, 0);
|
||||
m_decoderStimulations.initialize(*this, 1);
|
||||
|
||||
m_encoder.initialize(*this, 0);
|
||||
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmERSPAverage::uninitialize()
|
||||
{
|
||||
m_encoder.uninitialize();
|
||||
m_decoderSpectrum.uninitialize();
|
||||
m_decoderStimulations.uninitialize();
|
||||
|
||||
for (auto& v : m_cachedSpectra) { for (auto m : v) { delete m; } }
|
||||
m_cachedSpectra.clear();
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmERSPAverage::processInput(const size_t /*index*/)
|
||||
{
|
||||
getBoxAlgorithmContext()->markAlgorithmAsReadyToProcess();
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmERSPAverage::process()
|
||||
{
|
||||
Kernel::IBoxIO& boxContext = this->getDynamicBoxContext();
|
||||
|
||||
for (size_t i = 0; i < boxContext.getInputChunkCount(1); ++i)
|
||||
{
|
||||
m_decoderStimulations.decode(i);
|
||||
if (m_decoderStimulations.isBufferReceived())
|
||||
{
|
||||
const auto stims = m_decoderStimulations.getOutputStimulationSet();
|
||||
for (size_t j = 0; j < stims->getStimulationCount(); ++j)
|
||||
{
|
||||
if (stims->getStimulationIdentifier(j) == m_epochingStim)
|
||||
{
|
||||
m_currentChunk = 0;
|
||||
m_numTrials++;
|
||||
}
|
||||
if (stims->getStimulationIdentifier(j) == m_computeStim) { computeAndSend(); }
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for (size_t i = 0; i < boxContext.getInputChunkCount(0); ++i)
|
||||
{
|
||||
m_decoderSpectrum.decode(i);
|
||||
|
||||
if (m_decoderSpectrum.isHeaderReceived())
|
||||
{
|
||||
const uint64_t samplingRate = m_decoderSpectrum.getOutputSamplingRate();
|
||||
m_encoder.getInputSamplingRate() = samplingRate;
|
||||
m_encoder.getInputFrequencyAbscissa()->copy(*m_decoderSpectrum.getOutputFrequencyAbscissa());
|
||||
m_encoder.getInputMatrix()->copyDescription(*m_decoderSpectrum.getOutputMatrix());
|
||||
m_encoder.encodeHeader();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
|
||||
if (m_decoderSpectrum.isBufferReceived())
|
||||
{
|
||||
const CMatrix* input = m_decoderSpectrum.getOutputMatrix();
|
||||
appendChunk(*input, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
|
||||
if (m_decoderSpectrum.isEndReceived())
|
||||
{
|
||||
m_encoder.encodeEnd();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmERSPAverage::appendChunk(const CMatrix& chunk, const uint64_t startTime, const uint64_t endTime)
|
||||
{
|
||||
if (m_cachedSpectra.size() <= m_currentChunk)
|
||||
{
|
||||
m_cachedSpectra.resize(m_currentChunk + 1);
|
||||
m_timestamps.resize(m_currentChunk + 1);
|
||||
}
|
||||
|
||||
CMatrix* matrixCopy = new CMatrix();
|
||||
matrixCopy->copy(chunk);
|
||||
m_cachedSpectra[m_currentChunk].push_back(matrixCopy);
|
||||
m_timestamps[m_currentChunk].start = startTime;
|
||||
m_timestamps[m_currentChunk].end = endTime;
|
||||
|
||||
m_currentChunk++;
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmERSPAverage::computeAndSend()
|
||||
{
|
||||
Kernel::IBoxIO& boxContext = this->getDynamicBoxContext();
|
||||
|
||||
double* outptr = m_encoder.getInputMatrix()->getBuffer();
|
||||
|
||||
this->getLogManager() << Kernel::LogLevel_Info << "Counted " << m_numTrials << " trials and " << m_cachedSpectra.size() << " spectra per trial.\n";
|
||||
|
||||
for (size_t i = 0; i < m_cachedSpectra.size(); ++i)
|
||||
{
|
||||
// Compute average for each slice
|
||||
const double divider = 1.0 / m_cachedSpectra[i].size();
|
||||
m_encoder.getInputMatrix()->resetBuffer();
|
||||
|
||||
for (auto mat : m_cachedSpectra[i])
|
||||
{
|
||||
const double* inptr = mat->getBuffer();
|
||||
for (size_t p = 0; p < mat->getBufferElementCount(); ++p) { outptr[p] += divider * inptr[p]; }
|
||||
delete mat;
|
||||
}
|
||||
|
||||
m_encoder.encodeBuffer();
|
||||
boxContext.markOutputAsReadyToSend(0, m_timestamps[i].start - m_timestamps[0].start, m_timestamps[i].end - m_timestamps[0].start);
|
||||
}
|
||||
|
||||
m_numTrials = 0;
|
||||
m_currentChunk = 0;
|
||||
m_cachedSpectra.clear();
|
||||
|
||||
return true;
|
||||
}
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+92
@@ -0,0 +1,92 @@
|
||||
#pragma once
|
||||
|
||||
#include "../../ovp_defines.h"
|
||||
#include <openvibe/ov_all.h>
|
||||
#include <toolkit/ovtk_all.h>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
class CBoxAlgorithmERSPAverage final : virtual public Toolkit::TBoxAlgorithm<IBoxAlgorithm>
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { delete this; }
|
||||
|
||||
bool initialize() override;
|
||||
bool uninitialize() override;
|
||||
bool processInput(const size_t index) override;
|
||||
bool process() override;
|
||||
|
||||
_IsDerivedFromClass_Final_(Toolkit::TBoxAlgorithm<IBoxAlgorithm>, OVP_ClassId_BoxAlgorithm_ERSPAverage)
|
||||
|
||||
protected:
|
||||
|
||||
bool appendChunk(const CMatrix& chunk, uint64_t startTime, uint64_t endTime);
|
||||
bool computeAndSend();
|
||||
|
||||
Toolkit::TSpectrumDecoder<CBoxAlgorithmERSPAverage> m_decoderSpectrum;
|
||||
Toolkit::TStimulationDecoder<CBoxAlgorithmERSPAverage> m_decoderStimulations;
|
||||
Toolkit::TSpectrumEncoder<CBoxAlgorithmERSPAverage> m_encoder;
|
||||
|
||||
struct STimestamp
|
||||
{
|
||||
uint64_t start, end;
|
||||
};
|
||||
|
||||
std::vector<std::vector<CMatrix*>> m_cachedSpectra;
|
||||
std::vector<STimestamp> m_timestamps;
|
||||
|
||||
size_t m_currentChunk = 0;
|
||||
size_t m_numTrials = 0;
|
||||
|
||||
uint64_t m_epochingStim = 0;
|
||||
uint64_t m_computeStim = 0;
|
||||
};
|
||||
|
||||
|
||||
class CBoxAlgorithmERSPAverageDesc final : virtual public IBoxAlgorithmDesc
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { }
|
||||
|
||||
CString getName() const override { return CString("ERSP Average"); }
|
||||
CString getAuthorName() const override { return CString("Jussi T. Lindgren"); }
|
||||
CString getAuthorCompanyName() const override { return CString("Inria"); }
|
||||
|
||||
CString getShortDescription() const override
|
||||
{
|
||||
return CString("Averages a sequence of spectra per trial across multiple trials. The result is a sequence starting from t=0.");
|
||||
}
|
||||
|
||||
CString getDetailedDescription() const override
|
||||
{
|
||||
return CString(
|
||||
"Example: Given an input sequence [t,s1,s2,t,s3,s4] for two trials with s* the spectra and t a stimulation denoting the trial start, the box returns 1/2*(s1+s3), 1/2*(s2+s4).");
|
||||
}
|
||||
|
||||
CString getCategory() const override { return CString("Signal processing/Basic"); }
|
||||
CString getVersion() const override { return CString("0.1"); }
|
||||
CString getStockItemName() const override { return CString("gtk-sort-ascending"); }
|
||||
|
||||
CIdentifier getCreatedClass() const override { return OVP_ClassId_BoxAlgorithm_ERSPAverage; }
|
||||
IPluginObject* create() override { return new CBoxAlgorithmERSPAverage; }
|
||||
|
||||
bool getBoxPrototype(Kernel::IBoxProto& prototype) const override
|
||||
{
|
||||
prototype.addInput("Input spectra", OV_TypeId_Spectrum);
|
||||
prototype.addInput("Control stream", OV_TypeId_Stimulations);
|
||||
prototype.addOutput("Output spectra", OV_TypeId_Spectrum);
|
||||
|
||||
prototype.addSetting("Trial start marker", OV_TypeId_Stimulation, "OVTK_GDF_Start_Of_Trial", false);
|
||||
prototype.addSetting("Computation trigger", OV_TypeId_Stimulation, "OVTK_StimulationId_ExperimentStop", false);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(IBoxAlgorithmDesc, OVP_ClassId_BoxAlgorithm_ERSPAverageDesc)
|
||||
};
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+195
@@ -0,0 +1,195 @@
|
||||
#include "ovpCBoxAlgorithmEpochVariance.h"
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
bool CEpochVariance::initialize()
|
||||
{
|
||||
CIdentifier inputTypeID;
|
||||
getStaticBoxContext().getInputType(0, inputTypeID);
|
||||
if (inputTypeID == OV_TypeId_StreamedMatrix)
|
||||
{
|
||||
m_decoder = &getAlgorithmManager().getAlgorithm(getAlgorithmManager().createAlgorithm(OVP_GD_ClassId_Algorithm_StreamedMatrixDecoder));
|
||||
m_encoder = &getAlgorithmManager().getAlgorithm(getAlgorithmManager().createAlgorithm(OVP_GD_ClassId_Algorithm_StreamedMatrixEncoder));
|
||||
m_encoderForVariance = &getAlgorithmManager().getAlgorithm(getAlgorithmManager().createAlgorithm(OVP_GD_ClassId_Algorithm_StreamedMatrixEncoder));
|
||||
m_encoderForConfidenceBound = &getAlgorithmManager().
|
||||
getAlgorithm(getAlgorithmManager().createAlgorithm(OVP_GD_ClassId_Algorithm_StreamedMatrixEncoder));
|
||||
}
|
||||
else if (inputTypeID == OV_TypeId_FeatureVector)
|
||||
{
|
||||
m_decoder = &getAlgorithmManager().getAlgorithm(getAlgorithmManager().createAlgorithm(OVP_GD_ClassId_Algorithm_FeatureVectorDecoder));
|
||||
m_encoder = &getAlgorithmManager().getAlgorithm(getAlgorithmManager().createAlgorithm(OVP_GD_ClassId_Algorithm_FeatureVectorEncoder));
|
||||
m_encoderForVariance = &getAlgorithmManager().getAlgorithm(getAlgorithmManager().createAlgorithm(OVP_GD_ClassId_Algorithm_FeatureVectorEncoder));
|
||||
m_encoderForConfidenceBound = &getAlgorithmManager().getAlgorithm(getAlgorithmManager().createAlgorithm(OVP_GD_ClassId_Algorithm_FeatureVectorEncoder));
|
||||
}
|
||||
else if (inputTypeID == OV_TypeId_Signal)
|
||||
{
|
||||
m_decoder = &getAlgorithmManager().getAlgorithm(getAlgorithmManager().createAlgorithm(OVP_GD_ClassId_Algorithm_SignalDecoder));
|
||||
m_encoder = &getAlgorithmManager().getAlgorithm(getAlgorithmManager().createAlgorithm(OVP_GD_ClassId_Algorithm_SignalEncoder));
|
||||
m_encoderForVariance = &getAlgorithmManager().getAlgorithm(getAlgorithmManager().createAlgorithm(OVP_GD_ClassId_Algorithm_SignalEncoder));
|
||||
m_encoderForConfidenceBound = &getAlgorithmManager().getAlgorithm(getAlgorithmManager().createAlgorithm(OVP_GD_ClassId_Algorithm_SignalEncoder));
|
||||
}
|
||||
else if (inputTypeID == OV_TypeId_Spectrum)
|
||||
{
|
||||
m_decoder = &getAlgorithmManager().getAlgorithm(getAlgorithmManager().createAlgorithm(OVP_GD_ClassId_Algorithm_SpectrumDecoder));
|
||||
m_encoder = &getAlgorithmManager().getAlgorithm(getAlgorithmManager().createAlgorithm(OVP_GD_ClassId_Algorithm_SpectrumEncoder));
|
||||
m_encoderForVariance = &getAlgorithmManager().getAlgorithm(getAlgorithmManager().createAlgorithm(OVP_GD_ClassId_Algorithm_SpectrumDecoder));
|
||||
m_encoderForConfidenceBound = &getAlgorithmManager().getAlgorithm(getAlgorithmManager().createAlgorithm(OVP_GD_ClassId_Algorithm_SpectrumDecoder));
|
||||
}
|
||||
else { return false; }
|
||||
m_decoder->initialize();
|
||||
m_encoder->initialize();
|
||||
m_encoderForVariance->initialize();
|
||||
m_encoderForConfidenceBound->initialize();
|
||||
|
||||
m_matrixVariance = &getAlgorithmManager().getAlgorithm(getAlgorithmManager().createAlgorithm(OVP_ClassId_Algorithm_MatrixVariance));
|
||||
m_matrixVariance->initialize();
|
||||
|
||||
if (inputTypeID == OV_TypeId_StreamedMatrix) { }
|
||||
else if (inputTypeID == OV_TypeId_FeatureVector) { }
|
||||
else if (inputTypeID == OV_TypeId_Signal)
|
||||
{
|
||||
m_encoder->getInputParameter(OVP_GD_Algorithm_SignalEncoder_InputParameterId_Sampling)->setReferenceTarget(
|
||||
m_decoder->getOutputParameter(OVP_GD_Algorithm_SignalDecoder_OutputParameterId_Sampling));
|
||||
m_encoderForVariance->getInputParameter(OVP_GD_Algorithm_SignalEncoder_InputParameterId_Sampling)->setReferenceTarget(
|
||||
m_decoder->getOutputParameter(OVP_GD_Algorithm_SignalDecoder_OutputParameterId_Sampling));
|
||||
m_encoderForConfidenceBound->getInputParameter(OVP_GD_Algorithm_SignalEncoder_InputParameterId_Sampling)->setReferenceTarget(
|
||||
m_decoder->getOutputParameter(OVP_GD_Algorithm_SignalDecoder_OutputParameterId_Sampling));
|
||||
}
|
||||
else if (inputTypeID == OV_TypeId_Spectrum)
|
||||
{
|
||||
m_encoder->getInputParameter(OVP_GD_Algorithm_SpectrumEncoder_InputParameterId_FrequencyAbscissa)->setReferenceTarget(
|
||||
m_decoder->getOutputParameter(OVP_GD_Algorithm_SpectrumDecoder_OutputParameterId_FrequencyAbscissa));
|
||||
m_encoderForVariance->getInputParameter(OVP_GD_Algorithm_SpectrumEncoder_InputParameterId_FrequencyAbscissa)->setReferenceTarget(
|
||||
m_decoder->getOutputParameter(OVP_GD_Algorithm_SpectrumDecoder_OutputParameterId_FrequencyAbscissa));
|
||||
m_encoderForConfidenceBound->getInputParameter(OVP_GD_Algorithm_SpectrumEncoder_InputParameterId_FrequencyAbscissa)->setReferenceTarget(
|
||||
m_decoder->
|
||||
getOutputParameter(OVP_GD_Algorithm_SpectrumDecoder_OutputParameterId_FrequencyAbscissa));
|
||||
|
||||
m_encoder->getInputParameter(OVP_GD_Algorithm_SpectrumEncoder_InputParameterId_Sampling)->setReferenceTarget(
|
||||
m_decoder->getOutputParameter(OVP_GD_Algorithm_SpectrumDecoder_OutputParameterId_Sampling));
|
||||
m_encoderForVariance->getInputParameter(OVP_GD_Algorithm_SpectrumEncoder_InputParameterId_Sampling)->setReferenceTarget(
|
||||
m_decoder->getOutputParameter(OVP_GD_Algorithm_SpectrumDecoder_OutputParameterId_Sampling));
|
||||
m_encoderForConfidenceBound->getInputParameter(OVP_GD_Algorithm_SpectrumEncoder_InputParameterId_Sampling)->setReferenceTarget(
|
||||
m_decoder->getOutputParameter(OVP_GD_Algorithm_SpectrumDecoder_OutputParameterId_Sampling));
|
||||
}
|
||||
|
||||
ip_averagingMethod.initialize(m_matrixVariance->getInputParameter(OVP_Algorithm_MatrixVariance_InputParameterId_AveragingMethod));
|
||||
ip_matrixCount.initialize(m_matrixVariance->getInputParameter(OVP_Algorithm_MatrixVariance_InputParameterId_MatrixCount));
|
||||
ip_SignificanceLevel.initialize(m_matrixVariance->getInputParameter(OVP_Algorithm_MatrixVariance_InputParameterId_SignificanceLevel));
|
||||
|
||||
ip_averagingMethod = uint64_t(FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 0));
|
||||
ip_matrixCount = uint64_t(FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 1));
|
||||
ip_SignificanceLevel = double(FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 2));
|
||||
|
||||
m_matrixVariance->getInputParameter(OVP_Algorithm_MatrixVariance_InputParameterId_Matrix)->setReferenceTarget(
|
||||
m_decoder->getOutputParameter(OVP_GD_Algorithm_StreamedMatrixDecoder_OutputParameterId_Matrix));
|
||||
m_encoder->getInputParameter(OVP_GD_Algorithm_StreamedMatrixEncoder_InputParameterId_Matrix)->setReferenceTarget(
|
||||
m_matrixVariance->getOutputParameter(OVP_Algorithm_MatrixVariance_OutputParameterId_AveragedMatrix));
|
||||
m_encoderForVariance->getInputParameter(OVP_GD_Algorithm_StreamedMatrixEncoder_InputParameterId_Matrix)->setReferenceTarget(
|
||||
m_matrixVariance->getOutputParameter(OVP_Algorithm_MatrixVariance_OutputParameterId_Variance));
|
||||
m_encoderForConfidenceBound->getInputParameter(OVP_GD_Algorithm_StreamedMatrixEncoder_InputParameterId_Matrix)->setReferenceTarget(
|
||||
m_matrixVariance->getOutputParameter(OVP_Algorithm_MatrixVariance_OutputParameterId_ConfidenceBound));
|
||||
|
||||
|
||||
if (ip_matrixCount <= 0)
|
||||
{
|
||||
getLogManager() << Kernel::LogLevel_Error << "You should provide a positive number of epochs better than " << ip_matrixCount << "\n";
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CEpochVariance::uninitialize()
|
||||
{
|
||||
ip_averagingMethod.uninitialize();
|
||||
ip_matrixCount.uninitialize();
|
||||
|
||||
m_matrixVariance->uninitialize();
|
||||
m_encoder->uninitialize();
|
||||
m_encoderForVariance->uninitialize();
|
||||
m_encoderForConfidenceBound->uninitialize();
|
||||
m_decoder->uninitialize();
|
||||
|
||||
getAlgorithmManager().releaseAlgorithm(*m_matrixVariance);
|
||||
getAlgorithmManager().releaseAlgorithm(*m_encoder);
|
||||
getAlgorithmManager().releaseAlgorithm(*m_encoderForVariance);
|
||||
getAlgorithmManager().releaseAlgorithm(*m_encoderForConfidenceBound);
|
||||
getAlgorithmManager().releaseAlgorithm(*m_decoder);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CEpochVariance::processInput(const size_t /*index*/)
|
||||
{
|
||||
getBoxAlgorithmContext()->markAlgorithmAsReadyToProcess();
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CEpochVariance::process()
|
||||
{
|
||||
Kernel::IBoxIO& boxContext = getDynamicBoxContext();
|
||||
const size_t nInput = getStaticBoxContext().getInputCount();
|
||||
|
||||
for (size_t i = 0; i < nInput; ++i)
|
||||
{
|
||||
for (size_t j = 0; j < boxContext.getInputChunkCount(i); ++j)
|
||||
{
|
||||
Kernel::TParameterHandler<const IMemoryBuffer*> bufferHandle(
|
||||
m_decoder->getInputParameter(OVP_GD_Algorithm_StreamedMatrixDecoder_InputParameterId_MemoryBufferToDecode));
|
||||
Kernel::TParameterHandler<IMemoryBuffer*> obufferHandle(
|
||||
m_encoder->getOutputParameter(OVP_GD_Algorithm_StreamedMatrixEncoder_OutputParameterId_EncodedMemoryBuffer));
|
||||
Kernel::TParameterHandler<IMemoryBuffer*> obufferHandleForVariance(
|
||||
m_encoderForVariance->getOutputParameter(OVP_GD_Algorithm_StreamedMatrixEncoder_OutputParameterId_EncodedMemoryBuffer));
|
||||
Kernel::TParameterHandler<IMemoryBuffer*> bufferHandleForConfidenceBound(
|
||||
m_encoderForConfidenceBound->getOutputParameter(OVP_GD_Algorithm_StreamedMatrixEncoder_OutputParameterId_EncodedMemoryBuffer));
|
||||
bufferHandle = boxContext.getInputChunk(i, j);
|
||||
obufferHandle = boxContext.getOutputChunk(0);
|
||||
obufferHandleForVariance = boxContext.getOutputChunk(1);
|
||||
bufferHandleForConfidenceBound = boxContext.getOutputChunk(2);
|
||||
|
||||
m_decoder->process();
|
||||
if (m_decoder->isOutputTriggerActive(OVP_GD_Algorithm_StreamedMatrixDecoder_OutputTriggerId_ReceivedHeader))
|
||||
{
|
||||
m_matrixVariance->process(OVP_Algorithm_MatrixVariance_InputTriggerId_Reset);
|
||||
m_encoder->process(OVP_GD_Algorithm_StreamedMatrixEncoder_InputTriggerId_EncodeHeader);
|
||||
m_encoderForVariance->process(OVP_GD_Algorithm_StreamedMatrixEncoder_InputTriggerId_EncodeHeader);
|
||||
m_encoderForConfidenceBound->process(OVP_GD_Algorithm_StreamedMatrixEncoder_InputTriggerId_EncodeHeader);
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(i, j), boxContext.getInputChunkEndTime(i, j));
|
||||
boxContext.markOutputAsReadyToSend(1, boxContext.getInputChunkStartTime(i, j), boxContext.getInputChunkEndTime(i, j));
|
||||
boxContext.markOutputAsReadyToSend(2, boxContext.getInputChunkStartTime(i, j), boxContext.getInputChunkEndTime(i, j));
|
||||
}
|
||||
if (m_decoder->isOutputTriggerActive(OVP_GD_Algorithm_StreamedMatrixDecoder_OutputTriggerId_ReceivedBuffer))
|
||||
{
|
||||
m_matrixVariance->process(OVP_Algorithm_MatrixVariance_InputTriggerId_FeedMatrix);
|
||||
if (m_matrixVariance->isOutputTriggerActive(OVP_Algorithm_MatrixVariance_OutputTriggerId_AveragePerformed))
|
||||
{
|
||||
m_encoder->process(OVP_GD_Algorithm_StreamedMatrixEncoder_InputTriggerId_EncodeBuffer);
|
||||
m_encoderForVariance->process(OVP_GD_Algorithm_StreamedMatrixEncoder_InputTriggerId_EncodeBuffer);
|
||||
m_encoderForConfidenceBound->process(OVP_GD_Algorithm_StreamedMatrixEncoder_InputTriggerId_EncodeBuffer);
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(i, j), boxContext.getInputChunkEndTime(i, j));
|
||||
boxContext.markOutputAsReadyToSend(1, boxContext.getInputChunkStartTime(i, j), boxContext.getInputChunkEndTime(i, j));
|
||||
boxContext.markOutputAsReadyToSend(2, boxContext.getInputChunkStartTime(i, j), boxContext.getInputChunkEndTime(i, j));
|
||||
}
|
||||
}
|
||||
if (m_decoder->isOutputTriggerActive(OVP_GD_Algorithm_StreamedMatrixDecoder_OutputTriggerId_ReceivedEnd))
|
||||
{
|
||||
m_encoder->process(OVP_GD_Algorithm_StreamedMatrixEncoder_InputTriggerId_EncodeEnd);
|
||||
m_encoderForVariance->process(OVP_GD_Algorithm_StreamedMatrixEncoder_InputTriggerId_EncodeEnd);
|
||||
m_encoderForConfidenceBound->process(OVP_GD_Algorithm_StreamedMatrixEncoder_InputTriggerId_EncodeEnd);
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(i, j), boxContext.getInputChunkEndTime(i, j));
|
||||
boxContext.markOutputAsReadyToSend(1, boxContext.getInputChunkStartTime(i, j), boxContext.getInputChunkEndTime(i, j));
|
||||
boxContext.markOutputAsReadyToSend(2, boxContext.getInputChunkStartTime(i, j), boxContext.getInputChunkEndTime(i, j));
|
||||
}
|
||||
|
||||
boxContext.markInputAsDeprecated(i, j);
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+95
@@ -0,0 +1,95 @@
|
||||
#pragma once
|
||||
|
||||
#include "../../ovp_defines.h"
|
||||
|
||||
#include <openvibe/ov_all.h>
|
||||
#include <toolkit/ovtk_all.h>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
class CEpochVariance final : public Toolkit::TBoxAlgorithm<IBoxAlgorithm>
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { delete this; }
|
||||
|
||||
bool initialize() override;
|
||||
bool uninitialize() override;
|
||||
|
||||
bool processInput(const size_t index) override;
|
||||
bool process() override;
|
||||
|
||||
_IsDerivedFromClass_Final_(Toolkit::TBoxAlgorithm<IBoxAlgorithm>, OVP_ClassId_BoxAlgorithm_EpochVariance)
|
||||
|
||||
protected:
|
||||
|
||||
Kernel::IAlgorithmProxy* m_decoder = nullptr;
|
||||
Kernel::IAlgorithmProxy* m_encoder = nullptr;
|
||||
Kernel::IAlgorithmProxy* m_encoderForVariance = nullptr;
|
||||
Kernel::IAlgorithmProxy* m_encoderForConfidenceBound = nullptr;
|
||||
Kernel::IAlgorithmProxy* m_matrixVariance = nullptr;
|
||||
|
||||
Kernel::TParameterHandler<uint64_t> ip_matrixCount;
|
||||
Kernel::TParameterHandler<uint64_t> ip_averagingMethod;
|
||||
Kernel::TParameterHandler<double> ip_SignificanceLevel;
|
||||
};
|
||||
|
||||
class CEpochVarianceListener final : public Toolkit::TBoxListener<IBoxListener>
|
||||
{
|
||||
public:
|
||||
|
||||
bool onInputTypeChanged(Kernel::IBox& box, const size_t index) override
|
||||
{
|
||||
CIdentifier typeID = CIdentifier::undefined();
|
||||
box.getInputType(index, typeID);
|
||||
for (size_t i = 0; i < box.getOutputCount(); ++i) { box.setOutputType(i, typeID); }
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(Toolkit::TBoxListener<IBoxListener>, CIdentifier::undefined())
|
||||
};
|
||||
|
||||
class CEpochVarianceDesc final : public IBoxAlgorithmDesc
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { }
|
||||
CString getName() const override { return CString("Epoch variance"); }
|
||||
CString getAuthorName() const override { return CString("Dieter Devlaminck"); }
|
||||
CString getAuthorCompanyName() const override { return CString("INRIA"); }
|
||||
CString getShortDescription() const override { return CString("Computes variance of each sample over several epochs"); }
|
||||
CString getDetailedDescription() const override { return CString(""); }
|
||||
CString getCategory() const override { return CString("Signal processing/Basic"); }
|
||||
CString getVersion() const override { return CString("1.0"); }
|
||||
CString getStockItemName() const override { return CString("gtk-missing-image"); }
|
||||
|
||||
CIdentifier getCreatedClass() const override { return OVP_ClassId_BoxAlgorithm_EpochVariance; }
|
||||
IPluginObject* create() override { return new CEpochVariance(); }
|
||||
IBoxListener* createBoxListener() const override { return new CEpochVarianceListener; }
|
||||
void releaseBoxListener(IBoxListener* listener) const override { delete listener; }
|
||||
|
||||
bool getBoxPrototype(Kernel::IBoxProto& prototype) const override
|
||||
{
|
||||
prototype.addInput("Input epochs", OV_TypeId_StreamedMatrix);
|
||||
prototype.addOutput("Averaged epochs", OV_TypeId_StreamedMatrix);
|
||||
prototype.addOutput("Variance of epochs", OV_TypeId_StreamedMatrix);
|
||||
prototype.addOutput("Confidence bounds", OV_TypeId_StreamedMatrix);
|
||||
prototype.addSetting("Averaging type", OVP_TypeId_EpochAverageMethod, "Moving epoch average");
|
||||
prototype.addSetting("Epoch count", OV_TypeId_Integer, "4");
|
||||
prototype.addSetting("Significance level", OV_TypeId_Float, "0.01");
|
||||
prototype.addFlag(Kernel::BoxFlag_CanModifyInput);
|
||||
|
||||
prototype.addInputSupport(OV_TypeId_StreamedMatrix);
|
||||
prototype.addInputSupport(OV_TypeId_FeatureVector);
|
||||
prototype.addInputSupport(OV_TypeId_Signal);
|
||||
prototype.addInputSupport(OV_TypeId_Spectrum);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(IBoxAlgorithmDesc, OVP_ClassId_BoxAlgorithm_EpochVarianceDesc)
|
||||
};
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+125
@@ -0,0 +1,125 @@
|
||||
#include "ovpCBoxAlgorithmHilbert.h"
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
bool CBoxAlgorithmHilbert::initialize()
|
||||
{
|
||||
// Signal stream decoder
|
||||
m_decoder.initialize(*this, 0);
|
||||
// Signal stream encoder
|
||||
m_algo1Encoder.initialize(*this, 0);
|
||||
m_algo2Encoder.initialize(*this, 1);
|
||||
m_algo3Encoder.initialize(*this, 2);
|
||||
|
||||
m_hilbertAlgo = &this->getAlgorithmManager().getAlgorithm(this->getAlgorithmManager().createAlgorithm(OVP_ClassId_Algorithm_HilbertTransform));
|
||||
m_hilbertAlgo->initialize();
|
||||
|
||||
ip_signalMatrix.initialize(m_hilbertAlgo->getInputParameter(OVP_Algorithm_HilbertTransform_InputParameterId_Matrix));
|
||||
op_hilbertMatrix.initialize(m_hilbertAlgo->getOutputParameter(OVP_Algorithm_HilbertTransform_OutputParameterId_HilbertMatrix));
|
||||
op_envelopeMatrix.initialize(m_hilbertAlgo->getOutputParameter(OVP_Algorithm_HilbertTransform_OutputParameterId_EnvelopeMatrix));
|
||||
op_phaseMatrix.initialize(m_hilbertAlgo->getOutputParameter(OVP_Algorithm_HilbertTransform_OutputParameterId_PhaseMatrix));
|
||||
|
||||
ip_signalMatrix.setReferenceTarget(m_decoder.getOutputMatrix());
|
||||
|
||||
m_algo1Encoder.getInputSamplingRate().setReferenceTarget(m_decoder.getOutputSamplingRate());
|
||||
m_algo2Encoder.getInputSamplingRate().setReferenceTarget(m_decoder.getOutputSamplingRate());
|
||||
m_algo3Encoder.getInputSamplingRate().setReferenceTarget(m_decoder.getOutputSamplingRate());
|
||||
|
||||
m_algo1Encoder.getInputMatrix().setReferenceTarget(op_hilbertMatrix);
|
||||
m_algo2Encoder.getInputMatrix().setReferenceTarget(op_envelopeMatrix);
|
||||
m_algo3Encoder.getInputMatrix().setReferenceTarget(op_phaseMatrix);
|
||||
|
||||
return true;
|
||||
}
|
||||
/*******************************************************************************/
|
||||
|
||||
bool CBoxAlgorithmHilbert::uninitialize()
|
||||
{
|
||||
m_decoder.uninitialize();
|
||||
m_algo1Encoder.uninitialize();
|
||||
m_algo2Encoder.uninitialize();
|
||||
m_algo3Encoder.uninitialize();
|
||||
|
||||
ip_signalMatrix.uninitialize();
|
||||
op_hilbertMatrix.uninitialize();
|
||||
op_envelopeMatrix.uninitialize();
|
||||
op_phaseMatrix.uninitialize();
|
||||
|
||||
m_hilbertAlgo->uninitialize();
|
||||
this->getAlgorithmManager().releaseAlgorithm(*m_hilbertAlgo);
|
||||
|
||||
return true;
|
||||
}
|
||||
/*******************************************************************************/
|
||||
|
||||
|
||||
bool CBoxAlgorithmHilbert::processInput(const size_t /*index*/)
|
||||
{
|
||||
getBoxAlgorithmContext()->markAlgorithmAsReadyToProcess();
|
||||
return true;
|
||||
}
|
||||
/*******************************************************************************/
|
||||
|
||||
bool CBoxAlgorithmHilbert::process()
|
||||
{
|
||||
Kernel::IBoxIO& boxContext = this->getDynamicBoxContext();
|
||||
|
||||
//iterate over all chunk on input 0
|
||||
for (size_t i = 0; i < boxContext.getInputChunkCount(0); ++i)
|
||||
{
|
||||
// decode the chunk i on input 0
|
||||
m_decoder.decode(i);
|
||||
// the decoder may have decoded 3 different parts : the header, a buffer or the end of stream.
|
||||
if (m_decoder.isHeaderReceived())
|
||||
{
|
||||
// Header received
|
||||
m_hilbertAlgo->process(OVP_Algorithm_HilbertTransform_InputTriggerId_Initialize);
|
||||
|
||||
// Pass the header to the next boxes, by encoding a header on the output 0:
|
||||
m_algo1Encoder.encodeHeader();
|
||||
m_algo2Encoder.encodeHeader();
|
||||
m_algo3Encoder.encodeHeader();
|
||||
|
||||
// send the output chunk containing the header. The dates are the same as the input chunk:
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
boxContext.markOutputAsReadyToSend(1, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
boxContext.markOutputAsReadyToSend(2, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
if (m_decoder.isBufferReceived())
|
||||
{
|
||||
m_hilbertAlgo->process(OVP_Algorithm_HilbertTransform_InputTriggerId_Process);
|
||||
|
||||
// Encode the output buffer :
|
||||
m_algo1Encoder.encodeBuffer();
|
||||
m_algo2Encoder.encodeBuffer();
|
||||
m_algo3Encoder.encodeBuffer();
|
||||
|
||||
// and send it to the next boxes :
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
boxContext.markOutputAsReadyToSend(1, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
boxContext.markOutputAsReadyToSend(2, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
if (m_decoder.isEndReceived())
|
||||
{
|
||||
// End of stream received. This happens only once when pressing "stop". Just pass it to the next boxes so they receive the message :
|
||||
m_algo1Encoder.encodeEnd();
|
||||
m_algo2Encoder.encodeEnd();
|
||||
m_algo3Encoder.encodeEnd();
|
||||
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
boxContext.markOutputAsReadyToSend(1, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
boxContext.markOutputAsReadyToSend(2, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
|
||||
// The current input chunk has been processed, and automaticcaly discarded.
|
||||
// you don't need to call "boxContext.markInputAsDeprecated(0, i);"
|
||||
}
|
||||
|
||||
|
||||
return true;
|
||||
}
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+102
@@ -0,0 +1,102 @@
|
||||
#pragma once
|
||||
|
||||
#include "../../ovp_defines.h"
|
||||
|
||||
#include <openvibe/ov_all.h>
|
||||
#include <toolkit/ovtk_all.h>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
/**
|
||||
* \class CBoxAlgorithmHilbert
|
||||
* \author Alison Cellard (Inria)
|
||||
* \date Thu Jun 6 13:47:53 2013
|
||||
* \brief The class CBoxAlgorithmHilbert describes the box Phase and Envelope.
|
||||
*
|
||||
*/
|
||||
class CBoxAlgorithmHilbert final : virtual public Toolkit::TBoxAlgorithm<IBoxAlgorithm>
|
||||
{
|
||||
public:
|
||||
void release() override { delete this; }
|
||||
|
||||
bool initialize() override;
|
||||
bool uninitialize() override;
|
||||
|
||||
bool processInput(const size_t index) override;
|
||||
|
||||
bool process() override;
|
||||
|
||||
// As we do with any class in openvibe, we use the macro below
|
||||
// to associate this box to an unique identifier.
|
||||
// The inheritance information is also made available,
|
||||
// as we provide the superclass Toolkit::TBoxAlgorithm < IBoxAlgorithm >
|
||||
_IsDerivedFromClass_Final_(Toolkit::TBoxAlgorithm<IBoxAlgorithm>, OVP_ClassId_BoxAlgorithm_Hilbert)
|
||||
|
||||
protected:
|
||||
|
||||
// Signal stream decoder
|
||||
Toolkit::TSignalDecoder<CBoxAlgorithmHilbert> m_decoder;
|
||||
// Signal stream encoder
|
||||
Toolkit::TSignalEncoder<CBoxAlgorithmHilbert> m_algo1Encoder;
|
||||
Toolkit::TSignalEncoder<CBoxAlgorithmHilbert> m_algo2Encoder;
|
||||
Toolkit::TSignalEncoder<CBoxAlgorithmHilbert> m_algo3Encoder;
|
||||
|
||||
Kernel::IAlgorithmProxy* m_hilbertAlgo = nullptr;
|
||||
|
||||
Kernel::TParameterHandler<CMatrix*> ip_signalMatrix;
|
||||
Kernel::TParameterHandler<CMatrix*> op_hilbertMatrix;
|
||||
Kernel::TParameterHandler<CMatrix*> op_envelopeMatrix;
|
||||
Kernel::TParameterHandler<CMatrix*> op_phaseMatrix;
|
||||
};
|
||||
|
||||
/**
|
||||
* \class CBoxAlgorithmHilbertDesc
|
||||
* \author Alison Cellard (Inria)
|
||||
* \date Thu Jun 6 13:47:53 2013
|
||||
* \brief Descriptor of the box Phase and Envelope.
|
||||
*
|
||||
*/
|
||||
class CBoxAlgorithmHilbertDesc final : virtual public IBoxAlgorithmDesc
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { }
|
||||
|
||||
CString getName() const override { return CString("Hilbert Transform"); }
|
||||
CString getAuthorName() const override { return CString("Alison Cellard"); }
|
||||
CString getAuthorCompanyName() const override { return CString("Inria"); }
|
||||
|
||||
CString getShortDescription() const override { return CString("Hilbert transform, Phase and Envelope from discrete-time analytic signal using Hilbert"); }
|
||||
|
||||
CString getDetailedDescription() const override
|
||||
{
|
||||
return CString("Return Hilbert transform, phase and envelope of the input signal using analytic signal computation");
|
||||
}
|
||||
|
||||
CString getCategory() const override { return CString("Signal processing/Basic"); }
|
||||
CString getVersion() const override { return CString("1"); }
|
||||
CString getStockItemName() const override { return CString("gtk-new"); }
|
||||
|
||||
CIdentifier getCreatedClass() const override { return OVP_ClassId_BoxAlgorithm_Hilbert; }
|
||||
IPluginObject* create() override { return new CBoxAlgorithmHilbert; }
|
||||
|
||||
|
||||
bool getBoxPrototype(Kernel::IBoxProto& prototype) const override
|
||||
{
|
||||
prototype.addInput("Input Signal",OV_TypeId_Signal);
|
||||
|
||||
prototype.addOutput("Hilbert Transform", OV_TypeId_Signal);
|
||||
prototype.addOutput("Envelope",OV_TypeId_Signal);
|
||||
prototype.addOutput("Phase",OV_TypeId_Signal);
|
||||
|
||||
prototype.addFlag(OV_AttributeId_Box_FlagIsUnstable);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(IBoxAlgorithmDesc, OVP_ClassId_BoxAlgorithm_HilbertDesc)
|
||||
};
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+183
@@ -0,0 +1,183 @@
|
||||
#if defined TARGET_HAS_ThirdPartyITPP
|
||||
|
||||
//#define __OpenViBEPlugins_BoxAlgorithm_IFFTbox_CPP__
|
||||
// to get ifft:
|
||||
#include <itpp/itsignal.h>
|
||||
#include "ovpCBoxAlgorithmIFFTbox.h"
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
|
||||
bool CBoxAlgorithmIFFTbox::initialize()
|
||||
{
|
||||
m_decoder[0].initialize(*this, 0); // Spectrum stream real part decoder
|
||||
m_decoder[1].initialize(*this, 1); // Spectrum stream imaginary part decoder
|
||||
m_encoder.initialize(*this, 0); // Signal stream encoder
|
||||
|
||||
m_nSample = 0;
|
||||
m_headerSent = false;
|
||||
|
||||
return true;
|
||||
}
|
||||
/*******************************************************************************/
|
||||
|
||||
bool CBoxAlgorithmIFFTbox::uninitialize()
|
||||
{
|
||||
m_decoder[0].uninitialize();
|
||||
m_decoder[1].uninitialize();
|
||||
m_encoder.uninitialize();
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmIFFTbox::processInput(const size_t /*index*/)
|
||||
{
|
||||
IDynamicBoxContext& boxContext = this->getDynamicBoxContext();
|
||||
const size_t nInput = this->getStaticBoxContext().getInputCount();
|
||||
|
||||
if (boxContext.getInputChunkCount(0) == 0) { return true; }
|
||||
const uint64_t start = boxContext.getInputChunkStartTime(0, 0);
|
||||
const uint64_t end = boxContext.getInputChunkEndTime(0, 0);
|
||||
for (size_t i = 1; i < nInput; ++i)
|
||||
{
|
||||
if (boxContext.getInputChunkCount(i) == 0) { return true; }
|
||||
|
||||
if (start != boxContext.getInputChunkStartTime(i, 0) || end != boxContext.getInputChunkEndTime(i, 0))
|
||||
{
|
||||
OV_WARNING_K("Chunk dates mismatch, check stream structure or parameters");
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
getBoxAlgorithmContext()->markAlgorithmAsReadyToProcess();
|
||||
|
||||
return true;
|
||||
}
|
||||
/*******************************************************************************/
|
||||
|
||||
bool CBoxAlgorithmIFFTbox::process()
|
||||
{
|
||||
// the dynamic box context describes the current state of the box inputs and outputs (i.e. the chunks)
|
||||
Kernel::IBoxIO& boxContext = this->getDynamicBoxContext();
|
||||
const size_t nInput = this->getStaticBoxContext().getInputCount();
|
||||
|
||||
size_t nHeader = 0;
|
||||
size_t nBuffer = 0;
|
||||
size_t nEnd = 0;
|
||||
|
||||
for (size_t i = 0; i < nInput; ++i)
|
||||
{
|
||||
m_decoder[i].decode(0);
|
||||
if (m_decoder[i].isHeaderReceived())
|
||||
{
|
||||
//detect if header of other input is already received
|
||||
if (0 == nHeader)
|
||||
{
|
||||
// Header received. This happens only once when pressing "play". For example with a StreamedMatrix input, you now know the dimension count, sizes, and labels of the matrix
|
||||
// ... maybe do some process ...
|
||||
m_channelsNumber = m_decoder[i].getOutputMatrix()->getDimensionSize(0);
|
||||
m_nSample = m_decoder[i].getOutputMatrix()->getDimensionSize(1);
|
||||
OV_ERROR_UNLESS_KRF(m_channelsNumber > 0 && m_nSample > 0, "Both dims of the input matrix must have positive size",
|
||||
Kernel::ErrorType::BadProcessing);
|
||||
|
||||
m_nSample = (m_nSample - 1) * 2;
|
||||
if (m_nSample == 0) { m_nSample = 1; }
|
||||
}
|
||||
else
|
||||
{
|
||||
OV_ERROR_UNLESS_KRF(
|
||||
m_decoder[0].getOutputMatrix()->isDescriptionEqual(*m_decoder[i].getOutputMatrix(), false),
|
||||
"The matrix components of the two streams have different properties, check stream structures or parameters",
|
||||
Kernel::ErrorType::BadProcessing);
|
||||
|
||||
OV_ERROR_UNLESS_KRF(
|
||||
m_decoder[0].getOutputFrequencyAbscissa()->isDescriptionEqual(*m_decoder[i].getOutputFrequencyAbscissa(), false),
|
||||
"The frequencies abscissas descriptors of the two streams have different properties, check stream structures or parameters",
|
||||
Kernel::ErrorType::BadProcessing);
|
||||
|
||||
OV_ERROR_UNLESS_KRF(
|
||||
m_decoder[0].getOutputMatrix()->getDimensionSize(1) == m_decoder[i].getOutputFrequencyAbscissa()->getDimensionSize(0),
|
||||
"Frequencies abscissas count " << m_decoder[i].getOutputFrequencyAbscissa()->getDimensionSize(0) <<
|
||||
" does not match the corresponding matrix chunk size " << m_decoder[0].getOutputMatrix()->getDimensionSize(1) <<
|
||||
", check stream structures or parameters", Kernel::ErrorType::BadProcessing);
|
||||
|
||||
OV_ERROR_UNLESS_KRF(m_decoder[0].getOutputSamplingRate(), "Sampling rate must be positive, check stream structures or parameters",
|
||||
Kernel::ErrorType::BadProcessing);
|
||||
|
||||
OV_ERROR_UNLESS_KRF(m_decoder[0].getOutputSamplingRate() == m_decoder[i].getOutputSamplingRate(),
|
||||
"Sampling rates don't match (" << m_decoder[0].getOutputSamplingRate() << " != " << m_decoder[i].getOutputSamplingRate() <<
|
||||
"), please check stream structures or parameters", Kernel::ErrorType::BadProcessing);
|
||||
}
|
||||
|
||||
nHeader++;
|
||||
}
|
||||
if (m_decoder[i].isBufferReceived()) { nBuffer++; }
|
||||
if (m_decoder[i].isEndReceived()) { nEnd++; }
|
||||
}
|
||||
|
||||
if ((nHeader && nHeader != nInput) || (nBuffer && nBuffer != nInput) || (nEnd && nEnd != nInput))
|
||||
{
|
||||
OV_WARNING_K("Stream structure mismatch");
|
||||
return false;
|
||||
}
|
||||
|
||||
if (nBuffer)
|
||||
{
|
||||
OV_ERROR_UNLESS_KRF(m_nSample, "Received buffer before header, shouldn't happen\n", Kernel::ErrorType::BadProcessing);
|
||||
|
||||
if (!m_headerSent)
|
||||
{
|
||||
m_signalBuffer.set_size(int(m_nSample));
|
||||
m_frequencyBuffer.set_size(int(m_nSample));
|
||||
|
||||
m_encoder.getInputSamplingRate() = m_decoder[0].getOutputSamplingRate();
|
||||
m_encoder.getInputMatrix()->resize(m_channelsNumber, m_nSample);
|
||||
for (size_t channel = 0; channel < m_channelsNumber; ++channel)
|
||||
{
|
||||
m_encoder.getInputMatrix()->setDimensionLabel(
|
||||
0, channel, m_decoder[0].getOutputMatrix()->getDimensionLabel(0, channel));
|
||||
}
|
||||
|
||||
// Pass the header to the next boxes, by encoding a header on the output 0:
|
||||
m_encoder.encodeHeader();
|
||||
// send the output chunk containing the header. The dates are the same as the input chunk:
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, 0), boxContext.getInputChunkEndTime(0, 0));
|
||||
|
||||
m_headerSent = true;
|
||||
}
|
||||
|
||||
const double* bufferInput0 = m_decoder[0].getOutputMatrix()->getBuffer();
|
||||
const double* bufferInput1 = m_decoder[1].getOutputMatrix()->getBuffer();
|
||||
|
||||
for (size_t channel = 0; channel < m_channelsNumber; ++channel)
|
||||
{
|
||||
for (size_t j = 0; j < m_nSample; ++j)
|
||||
{
|
||||
m_frequencyBuffer[j].real(bufferInput0[int(channel * m_nSample + j)]);
|
||||
m_frequencyBuffer[j].imag(bufferInput1[int(channel * m_nSample + j)]);
|
||||
}
|
||||
|
||||
m_signalBuffer = ifft_real(m_frequencyBuffer);
|
||||
|
||||
double* bufferOutput = m_encoder.getInputMatrix()->getBuffer();
|
||||
for (size_t j = 0; j < m_nSample; ++j) { bufferOutput[int(channel * m_nSample + j)] = m_signalBuffer[j]; }
|
||||
}
|
||||
m_encoder.encodeBuffer();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, 0), boxContext.getInputChunkEndTime(0, 0));
|
||||
}
|
||||
if (nEnd)
|
||||
{
|
||||
// End of stream received. This happens only once when pressing "stop". Just pass it to the next boxes so they receive the message :
|
||||
m_encoder.encodeEnd();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, 0), boxContext.getInputChunkEndTime(0, 0));
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
#endif //TARGET_HAS_ThirdPartyITPP
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+94
@@ -0,0 +1,94 @@
|
||||
#pragma once
|
||||
|
||||
#if defined TARGET_HAS_ThirdPartyITPP
|
||||
|
||||
//You may have to change this path to match your folder organisation
|
||||
#include "../../ovp_defines.h"
|
||||
|
||||
#include <openvibe/ov_all.h>
|
||||
#include <toolkit/ovtk_all.h>
|
||||
|
||||
#include <complex>
|
||||
|
||||
#include <itpp/itbase.h>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
/**
|
||||
* \class CBoxAlgorithmIFFTbox
|
||||
* \author Guillermo Andrade B. (INRIA)
|
||||
* \date Fri Jan 20 15:35:05 2012
|
||||
* \brief The class CBoxAlgorithmIFFTbox describes the box IFFT box.
|
||||
*
|
||||
*/
|
||||
class CBoxAlgorithmIFFTbox final : virtual public Toolkit::TBoxAlgorithm<IBoxAlgorithm>
|
||||
{
|
||||
public:
|
||||
void release() override { delete this; }
|
||||
|
||||
bool initialize() override;
|
||||
bool uninitialize() override;
|
||||
|
||||
bool processInput(const size_t index) override;
|
||||
bool process() override;
|
||||
|
||||
_IsDerivedFromClass_Final_(Toolkit::TBoxAlgorithm<IBoxAlgorithm>, OVP_ClassId_BoxAlgorithm_IFFTbox)
|
||||
|
||||
protected:
|
||||
// Codec algorithms specified in the skeleton-generator:
|
||||
Toolkit::TSpectrumDecoder<CBoxAlgorithmIFFTbox> m_decoder[2];
|
||||
Toolkit::TSignalEncoder<CBoxAlgorithmIFFTbox> m_encoder;
|
||||
private:
|
||||
itpp::Vec<std::complex<double>> m_frequencyBuffer;
|
||||
itpp::Vec<double> m_signalBuffer;
|
||||
size_t m_nSample = 0;
|
||||
size_t m_channelsNumber = 0;
|
||||
bool m_headerSent = false;
|
||||
};
|
||||
|
||||
/**
|
||||
* \class CBoxAlgorithmIFFTboxDesc
|
||||
* \author Guillermo Andrade B. (INRIA)
|
||||
* \date Fri Jan 20 15:35:05 2012
|
||||
* \brief Descriptor of the box IFFT box.
|
||||
*
|
||||
*/
|
||||
class CBoxAlgorithmIFFTboxDesc final : virtual public IBoxAlgorithmDesc
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { }
|
||||
|
||||
CString getName() const override { return CString("IFFT"); }
|
||||
CString getAuthorName() const override { return CString("Guillermo Andrade B."); }
|
||||
CString getAuthorCompanyName() const override { return CString("INRIA"); }
|
||||
CString getShortDescription() const override { return CString("Compute Inverse Fast Fourier Transformation"); }
|
||||
|
||||
CString getDetailedDescription() const override { return CString("Compute Inverse Fast Fourier Transformation (depends on ITPP/fftw)"); }
|
||||
|
||||
CString getCategory() const override { return CString("Signal processing/Spectral Analysis"); }
|
||||
CString getVersion() const override { return CString("0.2"); }
|
||||
CString getStockItemName() const override { return CString("gtk-execute"); }
|
||||
|
||||
CIdentifier getCreatedClass() const override { return OVP_ClassId_BoxAlgorithm_IFFTbox; }
|
||||
IPluginObject* create() override { return new CBoxAlgorithmIFFTbox; }
|
||||
|
||||
bool getBoxPrototype(Kernel::IBoxProto& prototype) const override
|
||||
{
|
||||
prototype.addInput("real part",OV_TypeId_Spectrum);
|
||||
prototype.addInput("imaginary part",OV_TypeId_Spectrum);
|
||||
|
||||
prototype.addOutput("Signal output",OV_TypeId_Signal);
|
||||
|
||||
prototype.addFlag(OV_AttributeId_Box_FlagIsUnstable);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(IBoxAlgorithmDesc, OVP_ClassId_BoxAlgorithm_IFFTboxDesc)
|
||||
};
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
#endif //TARGET_HAS_ThirdPartyITPP
|
||||
+111
@@ -0,0 +1,111 @@
|
||||
#include "ovpCBoxAlgorithmMatrixTranspose.h"
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
bool CBoxAlgorithmMatrixTranspose::initialize()
|
||||
{
|
||||
m_decoder.initialize(*this, 0);
|
||||
m_encoder.initialize(*this, 0);
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmMatrixTranspose::uninitialize()
|
||||
{
|
||||
m_encoder.uninitialize();
|
||||
m_decoder.uninitialize();
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmMatrixTranspose::processInput(const size_t /*index*/)
|
||||
{
|
||||
getBoxAlgorithmContext()->markAlgorithmAsReadyToProcess();
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmMatrixTranspose::process()
|
||||
{
|
||||
Kernel::IBoxIO& boxContext = this->getDynamicBoxContext();
|
||||
|
||||
for (size_t i = 0; i < boxContext.getInputChunkCount(0); ++i)
|
||||
{
|
||||
m_decoder.decode(i);
|
||||
|
||||
if (m_decoder.isHeaderReceived())
|
||||
{
|
||||
const size_t nDim = m_decoder.getOutputMatrix()->getDimensionCount();
|
||||
|
||||
const CMatrix* input = m_decoder.getOutputMatrix();
|
||||
CMatrix* output = m_encoder.getInputMatrix();
|
||||
|
||||
if (nDim == 1)
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Trace << "Upgrading your 1 dimensional matrix to 2 dimensions, [" << input->getDimensionSize(0) <<
|
||||
"x 1]\n";
|
||||
|
||||
output->resize(input->getDimensionSize(0), 1);
|
||||
|
||||
for (size_t j = 0; j < input->getDimensionSize(0); ++j) { output->setDimensionLabel(0, j, input->getDimensionLabel(0, j)); }
|
||||
output->setDimensionLabel(1, 0, "Dimension 0");
|
||||
}
|
||||
else if (nDim == 2)
|
||||
{
|
||||
output->resize(input->getDimensionSize(1), input->getDimensionSize(0));
|
||||
|
||||
for (size_t j = 0; j < output->getDimensionSize(0); ++j) { output->setDimensionLabel(0, j, input->getDimensionLabel(1, j)); }
|
||||
for (size_t j = 0; j < output->getDimensionSize(1); ++j) { output->setDimensionLabel(1, j, input->getDimensionLabel(0, j)); }
|
||||
}
|
||||
else
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "Only 1 and 2 dimensional matrices supported\n";
|
||||
return false;
|
||||
}
|
||||
|
||||
this->getLogManager() << Kernel::LogLevel_Trace << "Output matrix will be [" << output->getDimensionSize(0) << "x" << output->getDimensionSize(1) << "]\n";
|
||||
|
||||
m_encoder.encodeHeader();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
|
||||
if (m_decoder.isBufferReceived())
|
||||
{
|
||||
const CMatrix* input = m_decoder.getOutputMatrix();
|
||||
CMatrix* output = m_encoder.getInputMatrix();
|
||||
|
||||
if (input->getDimensionCount() == 1)
|
||||
{
|
||||
const double* iBuffer = input->getBuffer();
|
||||
double* oBuffer = output->getBuffer();
|
||||
|
||||
for (size_t j = 0; j < input->getBufferElementCount(); ++j) { oBuffer[j] = iBuffer[j]; }
|
||||
}
|
||||
else
|
||||
{
|
||||
// 2 dim
|
||||
const size_t nRows = input->getDimensionSize(0);
|
||||
const size_t nCols = input->getDimensionSize(1);
|
||||
|
||||
const double* iBuffer = input->getBuffer();
|
||||
double* oBuffer = output->getBuffer();
|
||||
|
||||
for (size_t j = 0; j < nRows; ++j) { for (size_t k = 0; k < nCols; ++k) { oBuffer[k * nRows + j] = iBuffer[j * nCols + k]; } }
|
||||
}
|
||||
|
||||
m_encoder.encodeBuffer();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
|
||||
|
||||
if (m_decoder.isEndReceived())
|
||||
{
|
||||
m_encoder.encodeEnd();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+65
@@ -0,0 +1,65 @@
|
||||
#pragma once
|
||||
|
||||
#include "../../ovp_defines.h"
|
||||
#include <openvibe/ov_all.h>
|
||||
#include <toolkit/ovtk_all.h>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
class CBoxAlgorithmMatrixTranspose final : virtual public Toolkit::TBoxAlgorithm<IBoxAlgorithm>
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { delete this; }
|
||||
|
||||
bool initialize() override;
|
||||
bool uninitialize() override;
|
||||
bool processInput(const size_t index) override;
|
||||
bool process() override;
|
||||
|
||||
_IsDerivedFromClass_Final_(Toolkit::TBoxAlgorithm<IBoxAlgorithm>, OVP_ClassId_BoxAlgorithm_MatrixTranspose)
|
||||
|
||||
protected:
|
||||
|
||||
Toolkit::TStreamedMatrixDecoder<CBoxAlgorithmMatrixTranspose> m_decoder;
|
||||
Toolkit::TStreamedMatrixEncoder<CBoxAlgorithmMatrixTranspose> m_encoder;
|
||||
};
|
||||
|
||||
|
||||
class CBoxAlgorithmMatrixTransposeDesc final : virtual public IBoxAlgorithmDesc
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { }
|
||||
|
||||
CString getName() const override { return CString("Matrix Transpose"); }
|
||||
CString getAuthorName() const override { return CString("Jussi T. Lindgren"); }
|
||||
CString getAuthorCompanyName() const override { return CString("Inria"); }
|
||||
CString getShortDescription() const override { return CString("Transposes each matrix of the input stream"); }
|
||||
|
||||
CString getDetailedDescription() const override
|
||||
{
|
||||
return CString("Only works for 1 and 2 dimensional matrices. One-dimensional matrixes will be upgraded to two dimensions: [N x 1]");
|
||||
}
|
||||
|
||||
CString getCategory() const override { return CString("Signal processing/Basic"); }
|
||||
CString getVersion() const override { return CString("1.0"); }
|
||||
CString getStockItemName() const override { return CString("gtk-sort-ascending"); }
|
||||
|
||||
CIdentifier getCreatedClass() const override { return OVP_ClassId_BoxAlgorithm_MatrixTranspose; }
|
||||
IPluginObject* create() override { return new CBoxAlgorithmMatrixTranspose; }
|
||||
|
||||
bool getBoxPrototype(Kernel::IBoxProto& prototype) const override
|
||||
{
|
||||
prototype.addInput("Input matrix", OV_TypeId_StreamedMatrix);
|
||||
prototype.addOutput("Output matrix", OV_TypeId_StreamedMatrix);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(IBoxAlgorithmDesc, OVP_ClassId_BoxAlgorithm_MatrixTransposeDesc)
|
||||
};
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+23
@@ -0,0 +1,23 @@
|
||||
#include "ovpCBoxAlgorithmNull.h"
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
bool CBoxAlgorithmNull::processInput(const size_t /*index*/)
|
||||
{
|
||||
getBoxAlgorithmContext()->markAlgorithmAsReadyToProcess();
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmNull::process()
|
||||
{
|
||||
Kernel::IBoxIO* boxContext = getBoxAlgorithmContext()->getDynamicBoxContext();
|
||||
const size_t nInput = getBoxAlgorithmContext()->getStaticBoxContext()->getInputCount();
|
||||
for (size_t i = 0; i < nInput; ++i) { for (size_t j = 0; j < boxContext->getInputChunkCount(i); ++j) { boxContext->markInputAsDeprecated(i, j); } }
|
||||
|
||||
return true;
|
||||
}
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+59
@@ -0,0 +1,59 @@
|
||||
#pragma once
|
||||
|
||||
#include <toolkit/ovtk_all.h>
|
||||
|
||||
#define OVP_ClassId_BoxAlgorithm_Null OpenViBE::CIdentifier(0x601118A8, 0x14BF700F)
|
||||
#define OVP_ClassId_BoxAlgorithm_NullDesc OpenViBE::CIdentifier(0x6BD21A21, 0x0A5E685A)
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
class CBoxAlgorithmNull final : public Toolkit::TBoxAlgorithm<IBoxAlgorithm>
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { delete this; }
|
||||
bool processInput(const size_t index) override;
|
||||
bool process() override;
|
||||
|
||||
_IsDerivedFromClass_Final_(IBoxAlgorithm, OVP_ClassId_BoxAlgorithm_Null)
|
||||
};
|
||||
|
||||
class CBoxAlgorithmNullDesc final : public IBoxAlgorithmDesc
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { }
|
||||
CString getName() const override { return CString("Null"); }
|
||||
CString getAuthorName() const override { return CString("Jussi T. Lindgren"); }
|
||||
CString getAuthorCompanyName() const override { return CString("Inria"); }
|
||||
|
||||
CString getShortDescription() const override { return CString("Consumes input and produces nothing. It can be used to show scenario design intent."); }
|
||||
|
||||
CString getDetailedDescription() const override
|
||||
{
|
||||
return CString("Directing to Null instead of leaving a box output unconnected may add a tiny overhead.");
|
||||
}
|
||||
|
||||
CString getCategory() const override { return CString("Signal processing/Basic"); }
|
||||
CString getVersion() const override { return CString("1.0"); }
|
||||
CString getSoftwareComponent() const override { return CString("openvibe-extras"); }
|
||||
CString getAddedSoftwareVersion() const override { return CString("0.0.0"); }
|
||||
CString getUpdatedSoftwareVersion() const override { return CString("0.0.0"); }
|
||||
|
||||
CIdentifier getCreatedClass() const override { return OVP_ClassId_BoxAlgorithm_Null; }
|
||||
IPluginObject* create() override { return new CBoxAlgorithmNull(); }
|
||||
|
||||
bool getBoxPrototype(Kernel::IBoxProto& prototype) const override
|
||||
{
|
||||
prototype.addInput("Input stream", OV_TypeId_EBMLStream);
|
||||
prototype.addFlag(Kernel::BoxFlag_CanAddInput);
|
||||
prototype.addFlag(Kernel::BoxFlag_CanModifyInput);
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(IBoxAlgorithmDesc, OVP_ClassId_BoxAlgorithm_NullDesc)
|
||||
};
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+247
@@ -0,0 +1,247 @@
|
||||
///-------------------------------------------------------------------------------------------------
|
||||
///
|
||||
/// \file CBoxAlgorithmConnectivityMeasure.cpp
|
||||
/// \brief Implementation of the Box Connectivity Measure.
|
||||
/// \author Arthur DESBOIS (INRIA).
|
||||
/// \version 0.0.1.
|
||||
/// \date Fri Oct 30 16:18:49 2020.
|
||||
///
|
||||
/// \copyright (C) 2020 INRIA
|
||||
///
|
||||
/// This program is free software: you can redistribute it and/or modify
|
||||
/// it under the terms of the GNU Affero General Public License as published
|
||||
/// by the Free Software Foundation, either version 3 of the License, or
|
||||
/// (at your option) any later version.
|
||||
///
|
||||
/// This program is distributed in the hope that it will be useful,
|
||||
/// but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
/// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
/// GNU Affero General Public License for more details.
|
||||
///
|
||||
/// You should have received a copy of the GNU Affero General Public License
|
||||
/// along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
///-------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
#include "CBoxAlgorithmConnectivityMeasure.hpp"
|
||||
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
bool CBoxAlgorithmConnectivityMeasure::initialize()
|
||||
{
|
||||
m_signalDecoder.initialize(*this, 0);
|
||||
m_matrixEncoder.initialize(*this, 0);
|
||||
|
||||
m_iMatrix = m_signalDecoder.getOutputMatrix();
|
||||
m_oMatrix = m_matrixEncoder.getInputMatrix();
|
||||
|
||||
// Settings
|
||||
m_metric = EConnectMetric(uint64_t(FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 0)));
|
||||
m_windowMethod = EConnectWindowMethod(uint64_t(FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 1)));
|
||||
m_windowLengthSeconds = double(FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 2));
|
||||
m_windowOverlap = int(FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 3));
|
||||
m_connectLengthSeconds = double(FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 4));
|
||||
m_connectOverlap = int(FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 5));
|
||||
m_fftSize = int(FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 6));
|
||||
m_dcRemoval = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 7);
|
||||
return true;
|
||||
}
|
||||
|
||||
/*******************************************************************************/
|
||||
|
||||
bool CBoxAlgorithmConnectivityMeasure::uninitialize()
|
||||
{
|
||||
m_signalDecoder.uninitialize();
|
||||
m_matrixEncoder.uninitialize();
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmConnectivityMeasure::processInput(const size_t)
|
||||
{
|
||||
getBoxAlgorithmContext()->markAlgorithmAsReadyToProcess();
|
||||
return true;
|
||||
}
|
||||
|
||||
/*******************************************************************************/
|
||||
|
||||
|
||||
bool CBoxAlgorithmConnectivityMeasure::process()
|
||||
{
|
||||
|
||||
// the static box context describes the box inputs, outputs, settings structures
|
||||
const Kernel::IBox& staticBoxContext = this->getStaticBoxContext();
|
||||
|
||||
// the dynamic box context describes the current state of the box inputs and outputs (i.e. the chunks)
|
||||
Kernel::IBoxIO& boxContext = this->getDynamicBoxContext();
|
||||
|
||||
//iterate over all chunk on input 0
|
||||
for (size_t i = 0; i < boxContext.getInputChunkCount(0); ++i)
|
||||
{
|
||||
m_signalDecoder.decode(i);
|
||||
|
||||
const uint64_t tStart = boxContext.getInputChunkStartTime(0, i); // Time Code Chunk Start
|
||||
const uint64_t tEnd = boxContext.getInputChunkEndTime(0, i); // Time Code Chunk End
|
||||
|
||||
if (m_signalDecoder.isHeaderReceived())
|
||||
{
|
||||
// Header received. This happens only once when pressing "play".
|
||||
|
||||
CMatrix* matrix = m_signalDecoder.getOutputMatrix(); // the StreamedMatrix of samples.
|
||||
uint64_t sampRate = m_signalDecoder.getOutputSamplingRate(); // the sampling rate of the signal
|
||||
|
||||
m_windowLength = std::floor((float) sampRate * m_windowLengthSeconds);
|
||||
m_connectLength = std::floor((float) sampRate * m_connectLengthSeconds);
|
||||
m_connectOverlapSamples = int(std::floor(double(m_connectLength) * double(m_connectOverlap) / 100.0));
|
||||
m_windowOverlapSamples = int(std::floor(double(m_windowLength) * double(m_windowOverlap) / 100.0));
|
||||
|
||||
m_nbChannels = matrix->getDimensionSize(0);
|
||||
const auto sampPerChan = matrix->getDimensionSize(1);
|
||||
|
||||
this->getLogManager() << Kernel::LogLevel_Debug << "HEADER : nChannels " << m_nbChannels << ", " << sampPerChan
|
||||
<< " samples, sampling rate " << sampRate << "\n";
|
||||
this->getLogManager() << Kernel::LogLevel_Debug << "Connectivity segments overlap percent/samples : "
|
||||
<< m_connectOverlap << " " << m_connectOverlapSamples << "\n";
|
||||
this->getLogManager() << Kernel::LogLevel_Debug << "Welch Windows overlap percent/samples : " << m_windowOverlap
|
||||
<< " " << m_windowOverlapSamples << "\n";
|
||||
|
||||
|
||||
// Vectors buffers init
|
||||
m_vectorXdBuffer.resize(m_nbChannels);
|
||||
m_signalChannelBuffers.resize(m_nbChannels);
|
||||
|
||||
this->getLogManager() << Kernel::LogLevel_Debug << "m_signalChannelBuffers.size() " << m_signalChannelBuffers.size()
|
||||
<< "\n";
|
||||
|
||||
// Connectivity algo class inits
|
||||
connectivityMeasure.initialize(m_metric, m_windowMethod, m_windowLength, m_windowOverlap, m_nbChannels,
|
||||
m_fftSize, m_dcRemoval);
|
||||
|
||||
matrix3DInit(*m_oMatrix, m_fftSize, m_nbChannels, m_nbChannels); // nbFreqs x nbChan x nbChan
|
||||
|
||||
m_matrixEncoder.encodeHeader(); // Pass the header to the next boxes
|
||||
|
||||
} else if (m_signalDecoder.isBufferReceived())
|
||||
{
|
||||
|
||||
CMatrix* matrix = m_signalDecoder.getOutputMatrix(); // the StreamedMatrix of samples.
|
||||
uint64_t sampRate = m_signalDecoder.getOutputSamplingRate(); // the sampling rate of the signal
|
||||
const auto sampPerChan = matrix->getDimensionSize(1);
|
||||
|
||||
// Accumulate buffers here and send a whole chunk to the connectivity algorithm
|
||||
const double* buffer = matrix->getBuffer();
|
||||
size_t idx = 0;
|
||||
std::vector<double> temp;
|
||||
for (size_t row = 0; row < m_nbChannels; ++row)
|
||||
{
|
||||
for (size_t col = 0; col < sampPerChan; ++col)
|
||||
{ // parse all columns in the buffer
|
||||
temp.push_back(buffer[idx++]);
|
||||
}
|
||||
m_signalChannelBuffers[row].insert(m_signalChannelBuffers[row].end(), temp.begin(), temp.end());
|
||||
temp.clear();
|
||||
}
|
||||
|
||||
this->getLogManager() << Kernel::LogLevel_Debug << "BUFFER : " << sampPerChan << " samples per " << m_nbChannels
|
||||
<< " channels, sampling rate " << sampRate << " // Signal buffers size : "
|
||||
<< m_signalChannelBuffers[0].size() << "\n";
|
||||
|
||||
// If enough data was accumulated, process it.
|
||||
if (m_signalChannelBuffers[0].size() >= m_connectLength)
|
||||
{
|
||||
|
||||
this->getLogManager() << Kernel::LogLevel_Debug << "Signal buffers : FULL (" << m_signalChannelBuffers[0].size()
|
||||
<< ")\n";
|
||||
|
||||
// Convert to Eigen container for easier use in algo
|
||||
for (size_t aa = 0; aa < m_nbChannels; ++aa)
|
||||
{
|
||||
m_vectorXdBuffer[aa] = Eigen::VectorXd::Map(m_signalChannelBuffers[aa].data(),
|
||||
m_signalChannelBuffers[aa].size());
|
||||
}
|
||||
|
||||
// Connectivity chunk overlap for next processing loop: Keep the overlapping part in the vectors, discard the rest
|
||||
for (size_t aa = 0; aa < m_nbChannels; ++aa)
|
||||
{
|
||||
m_signalChannelBuffers[aa].erase(m_signalChannelBuffers[aa].begin(),
|
||||
m_signalChannelBuffers[aa].begin() +
|
||||
(m_connectLength - m_connectOverlapSamples));
|
||||
}
|
||||
|
||||
// 3D Matrix init, vector of size (chan) of Matrices (chan x fftsize)
|
||||
std::vector <Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic>> connectivityMatrix(m_nbChannels,
|
||||
Eigen::MatrixXd(
|
||||
m_nbChannels,
|
||||
m_fftSize));
|
||||
|
||||
OV_ERROR_UNLESS_KRF(connectivityMeasure.process(m_vectorXdBuffer, connectivityMatrix),
|
||||
"Connectivity measurement error", Kernel::ErrorType::BadProcessing);
|
||||
|
||||
this->getLogManager() << Kernel::LogLevel_Debug << "Exited connectivityMeasure.process() : connect size "
|
||||
<< connectivityMatrix.size() << " x " << connectivityMatrix[0].rows() << " x "
|
||||
<< connectivityMatrix[0].cols() << "\n";
|
||||
|
||||
// Convert output matrix (nchan x nchan x fftsize) to matrix (fftsize x nchan x nchan)
|
||||
matrix3DConvert(connectivityMatrix, *m_oMatrix);
|
||||
|
||||
m_matrixEncoder.encodeBuffer();
|
||||
}
|
||||
|
||||
} else if (m_signalDecoder.isEndReceived())
|
||||
{
|
||||
m_matrixEncoder.encodeEnd();
|
||||
}
|
||||
|
||||
boxContext.markOutputAsReadyToSend(0, tStart, tEnd);
|
||||
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
void CBoxAlgorithmConnectivityMeasure::matrix3DInit(CMatrix& m, const size_t dim0, const size_t dim1, const size_t dim2)
|
||||
{
|
||||
m.setDimensionCount(3);
|
||||
m.setDimensionSize(0, dim0);
|
||||
m.setDimensionSize(1, dim1);
|
||||
m.setDimensionSize(2, dim2);
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmConnectivityMeasure::matrix3DConvert(const std::vector <Eigen::MatrixXd>& in, CMatrix& out)
|
||||
{
|
||||
if (in.size() == 0 || in[0].rows() == 0 || in[0].cols() == 0)
|
||||
{ return false; }
|
||||
const size_t nChan0 = in.size(), nChan1 = in[0].rows(), fftSize = in[0].cols();
|
||||
|
||||
if (out.getDimensionCount() != 3
|
||||
|| out.getDimensionSize(0) != fftSize
|
||||
|| out.getDimensionSize(1) != nChan1
|
||||
|| out.getDimensionSize(2) != nChan0)
|
||||
{
|
||||
out.setDimensionCount(3);
|
||||
out.setDimensionSize(0, fftSize);
|
||||
out.setDimensionSize(1, nChan1);
|
||||
out.setDimensionSize(2, nChan0);
|
||||
}
|
||||
|
||||
size_t idx = 0;
|
||||
double* buffer = out.getBuffer();
|
||||
for (size_t fftIdx = 0; fftIdx < fftSize; ++fftIdx)
|
||||
{
|
||||
for (size_t chan1 = 0; chan1 < nChan1; ++chan1)
|
||||
{
|
||||
for (size_t chan0 = 0; chan0 < nChan0; ++chan0)
|
||||
{
|
||||
buffer[idx++] = in[chan0](chan1, fftIdx);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+160
@@ -0,0 +1,160 @@
|
||||
///-------------------------------------------------------------------------------------------------
|
||||
///
|
||||
/// \file CBoxAlgorithmConnectivityMeasure.hpp
|
||||
/// \brief Classes of the Box Connectivity Measure.
|
||||
/// \author Arthur DESBOIS (INRIA).
|
||||
/// \version 0.0.1.
|
||||
/// \date Fri Oct 30 16:18:49 2020.
|
||||
///
|
||||
/// \copyright (C) 2020 INRIA
|
||||
///
|
||||
/// This program is free software: you can redistribute it and/or modify
|
||||
/// it under the terms of the GNU Affero General Public License as published
|
||||
/// by the Free Software Foundation, either version 3 of the License, or
|
||||
/// (at your option) any later version.
|
||||
///
|
||||
/// This program is distributed in the hope that it will be useful,
|
||||
/// but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
/// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
/// GNU Affero General Public License for more details.
|
||||
///
|
||||
/// You should have received a copy of the GNU Affero General Public License
|
||||
/// along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
///-------------------------------------------------------------------------------------------------
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <Eigen/Dense>
|
||||
|
||||
#include "ovp_defines.h"
|
||||
#include <openvibe/ov_all.h>
|
||||
#include <toolkit/ovtk_all.h>
|
||||
#include "connectivityMeasure.hpp"
|
||||
|
||||
|
||||
#define OV_AttributeId_Box_FlagIsUnstable OpenViBE::CIdentifier(0x666FFFFF, 0x666FFFFF)
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
///
|
||||
/// \brief The class CBoxAlgorithmConnectivityMeasure describes the box Connectivity Measure.
|
||||
///
|
||||
class CBoxAlgorithmConnectivityMeasure final : virtual public Toolkit::TBoxAlgorithm<IBoxAlgorithm>
|
||||
{
|
||||
public:
|
||||
void release() override { delete this; }
|
||||
|
||||
bool initialize() override;
|
||||
|
||||
bool uninitialize() override;
|
||||
|
||||
bool processInput(const size_t index) override;
|
||||
|
||||
bool process() override;
|
||||
|
||||
_IsDerivedFromClass_Final_(Toolkit::TBoxAlgorithm<IBoxAlgorithm>, OVP_ClassId_BoxAlgorithm_ConnectivityMeasure)
|
||||
|
||||
protected:
|
||||
|
||||
// Algo class instance
|
||||
ConnectivityMeasure connectivityMeasure;
|
||||
|
||||
// Codecs
|
||||
Toolkit::TSignalDecoder <CBoxAlgorithmConnectivityMeasure> m_signalDecoder;
|
||||
Toolkit::TStreamedMatrixEncoder <CBoxAlgorithmConnectivityMeasure> m_matrixEncoder; // Output Matrix Codec
|
||||
|
||||
// Matrices
|
||||
CMatrix* m_iMatrix = nullptr; // Input Matrix pointer
|
||||
CMatrix* m_oMatrix = nullptr; // Output Matrix pointer
|
||||
|
||||
std::vector <Eigen::VectorXd> m_vectorXdBuffer; // Vector buffer, for connectivity segments
|
||||
std::vector <std::vector<double>> m_signalChannelBuffers;
|
||||
|
||||
Eigen::VectorXd m_window;
|
||||
|
||||
// Settings
|
||||
EConnectMetric m_metric = EConnectMetric::Coherence;
|
||||
EConnectWindowMethod m_windowMethod = EConnectWindowMethod::Hann;
|
||||
|
||||
float m_windowLengthSeconds = 0.25f; // size of one windowing (sec)
|
||||
float m_connectLengthSeconds = 0.5f; // size of one full connectivity estimation occurrence (sec)
|
||||
|
||||
int m_windowLength = 128; // size of one windowing (samples)
|
||||
int m_windowOverlap = 50; // overlap btw windows (%)
|
||||
int m_connectLength = 256; // size of one full connectivity estimation occurrence (samples)
|
||||
int m_connectOverlap = 50; // overlap btw connectivity measurements (%)
|
||||
int m_fftSize = 128; // FFT size (and nb of freq taps at the output)
|
||||
|
||||
int m_nbChannels = 0;
|
||||
|
||||
int m_connectOverlapSamples = 128;
|
||||
int m_windowOverlapSamples = 64;
|
||||
|
||||
bool m_dcRemoval = false;
|
||||
|
||||
private:
|
||||
///
|
||||
/// \brief Conversion from a vector of 2D Matrices into OpenViBE's CMatrix of 3 dimensions
|
||||
/// \param in std:vector<Eigen::Matrix> (vect of nChan x Matrices (nChan x fftsize)
|
||||
/// \param out CMatrix of 3 dimensions (fftsize x nchan x nchan)
|
||||
/// \return True if the conversion was successful, false otherwise
|
||||
bool matrix3DConvert(const std::vector <Eigen::MatrixXd>& in, CMatrix& out);
|
||||
|
||||
///
|
||||
/// \brief Initialises 3 dimensions matrix with the provided dimensions
|
||||
/// \param m The matrix to initialise
|
||||
/// \param dim1 dimension 1
|
||||
/// \param dim2 dimension 2
|
||||
/// \param dim3 dimension 3
|
||||
void matrix3DInit(CMatrix& m, const size_t dim1, const size_t dim2, const size_t dim3);
|
||||
|
||||
};
|
||||
|
||||
///
|
||||
/// \brief Descriptor of the box Connectivity Measure.
|
||||
///
|
||||
class CBoxAlgorithmConnectivityMeasureDesc final : virtual public IBoxAlgorithmDesc
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override {}
|
||||
CString getName() const override { return CString("Connectivity Measure"); }
|
||||
CString getAuthorName() const override { return CString("Arthur DESBOIS"); }
|
||||
CString getAuthorCompanyName() const override { return CString("INRIA"); }
|
||||
CString getShortDescription() const override { return CString("Connectivity Measure"); }
|
||||
CString getDetailedDescription() const override { return CString("Measure connectivity between pairs of channel"); }
|
||||
CString getCategory() const override { return CString("Signal processing/Connectivity"); }
|
||||
CString getVersion() const override { return CString("0.0.1"); }
|
||||
CString getStockItemName() const override { return CString(""); }
|
||||
CIdentifier getCreatedClass() const override { return OVP_ClassId_BoxAlgorithm_ConnectivityMeasure; }
|
||||
IPluginObject* create() override { return new CBoxAlgorithmConnectivityMeasure; }
|
||||
|
||||
bool getBoxPrototype(Kernel::IBoxProto& prototype) const override
|
||||
{
|
||||
prototype.addInput("Input signal", OV_TypeId_Signal);
|
||||
|
||||
prototype.addOutput("Connectivity Matrix", OV_TypeId_StreamedMatrix);
|
||||
|
||||
prototype.addSetting("Metric", OVP_TypeId_Connectivity_Metric,
|
||||
toString(EConnectMetric::Coherence).c_str());
|
||||
prototype.addSetting("Welch Window method", OV_TypeId_ConnectivityMeasure_WindowMethod, "Hann");
|
||||
prototype.addSetting("Welch Window Length (in sec)", OV_TypeId_Float, "0.25"); // s
|
||||
prototype.addSetting("Welch Window Overlap (in %)", OV_TypeId_Integer, "50"); // percent
|
||||
prototype.addSetting("Connectivity Measure Length (in sec)", OV_TypeId_Float, "0.5"); //s
|
||||
prototype.addSetting("Connectivity Measure Overlap (in %)", OV_TypeId_Integer, "50"); // percent
|
||||
prototype.addSetting("FFT size (frequency taps)", OV_TypeId_Integer, "128");
|
||||
prototype.addSetting("DC removal", OV_TypeId_Boolean, "false");
|
||||
|
||||
prototype.addFlag(OV_AttributeId_Box_FlagIsUnstable);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(IBoxAlgorithmDesc, OVP_ClassId_BoxAlgorithm_ConnectivityMeasureDesc)
|
||||
};
|
||||
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+379
@@ -0,0 +1,379 @@
|
||||
#if defined TARGET_HAS_ThirdPartyITPP
|
||||
|
||||
#include "ovpCBoxAlgorithmXDAWNSpatialFilterTrainer.h"
|
||||
|
||||
#include <complex>
|
||||
#include <sstream>
|
||||
#include <cstdio>
|
||||
#include <vector>
|
||||
#include <map>
|
||||
|
||||
#include <itpp/base/algebra/inv.h>
|
||||
#include <itpp/stat/misc_stat.h>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
// Taken from http://techlogbook.wordpress.com/2009/08/12/adding-generalized-eigenvalue-functions-to-it
|
||||
// http://techlogbook.wordpress.com/2009/08/12/calling-lapack-functions-from-c-codes
|
||||
// http://sourceforge.net/projects/itpp/forums/forum/115656/topic/3363490?message=7557038
|
||||
//
|
||||
// http://icl.cs.utk.edu/projectsfiles/f2j/javadoc/org/netlib/lapack/DSYGV.html
|
||||
// http://www.lassp.cornell.edu/sethna/GeneDynamics/NetworkCodeDocumentation/lapack_8h.html#a17
|
||||
|
||||
namespace {
|
||||
extern "C" {
|
||||
// This symbol comes from LAPACK
|
||||
/*
|
||||
void zggev_(char *jobvl, char *jobvr, int *n, std::complex<double> *a,
|
||||
int *lda, std::complex<double> *b, int *ldb, std::complex<double> *alpha,
|
||||
std::complex<double> *beta, std::complex<double> *vl,
|
||||
int *ldvl, std::complex<double> *vr, int *ldvr,
|
||||
std::complex<double> *work, int *lwork, double *rwork, int *info);
|
||||
*/
|
||||
int dsygv_(int* itype, char* jobz, char* uplo, int* n, double* a, int* lda, double* b, int* ldb, double* w, double* work, int* lwork, int* info);
|
||||
}
|
||||
} // namespace
|
||||
|
||||
namespace itppext {
|
||||
bool eig(const itpp::mat& A, const itpp::mat& B, itpp::vec& d, itpp::mat& V)
|
||||
{
|
||||
it_assert_debug(A.rows() == A.cols(), "eig: Matrix A is not square");
|
||||
it_assert_debug(B.rows() == B.cols(), "eig: Matrix B is not square");
|
||||
it_assert_debug(A.rows() == B.cols(), "eig: Matrix A and B don't have the same size");
|
||||
|
||||
const int worksize = 4 * A.rows(); // This may be chosen better!
|
||||
itpp::mat lA(A);
|
||||
itpp::mat lB(B);
|
||||
itpp::vec lW(A.rows());
|
||||
itpp::vec lWork(worksize);
|
||||
lW.zeros();
|
||||
lWork.zeros();
|
||||
|
||||
int itype = 1; // 1: Ax=lBx 2: ABx=lx 3: BAx=lx
|
||||
char jobz = 'V', uplo = 'U';
|
||||
int n = lA.rows();
|
||||
double* a = lA._data();
|
||||
int lda = n;
|
||||
double* b = lB._data();
|
||||
int ldb = n;
|
||||
double* w = lW._data();
|
||||
int lwork = worksize;
|
||||
double* work = lWork._data();
|
||||
int info = 0;
|
||||
|
||||
dsygv_(&itype, &jobz, &uplo, &n, a, &lda, b, &ldb, w, work, &lwork, &info);
|
||||
|
||||
d = lW;
|
||||
V = lA;
|
||||
|
||||
return (info == 0);
|
||||
}
|
||||
|
||||
itpp::mat convert(const CMatrix& matrix)
|
||||
{
|
||||
itpp::mat res(matrix.getDimensionSize(1), matrix.getDimensionSize(0));
|
||||
memcpy(res._data(), matrix.getBuffer(), matrix.getBufferElementCount() * sizeof(double));
|
||||
return res.transpose();
|
||||
}
|
||||
} // namespace itppext
|
||||
|
||||
bool CBoxAlgorithmXDAWNSpatialFilterTrainer::initialize()
|
||||
{
|
||||
m_stimulationDecoder.initialize(*this, 0);
|
||||
m_signalDecoder.initialize(*this, 1);
|
||||
m_evokedPotentialDecoder.initialize(*this, 2);
|
||||
|
||||
m_encoder.initialize(*this, 0);
|
||||
|
||||
m_stimID = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 0);
|
||||
m_spatialFilterConfigurationFilename = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 1);
|
||||
m_filterDim = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 2);
|
||||
m_saveAsBoxConfig = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 3);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmXDAWNSpatialFilterTrainer::uninitialize()
|
||||
{
|
||||
m_evokedPotentialDecoder.uninitialize();
|
||||
m_signalDecoder.uninitialize();
|
||||
m_stimulationDecoder.uninitialize();
|
||||
m_encoder.uninitialize();
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmXDAWNSpatialFilterTrainer::processInput(const size_t /*index*/)
|
||||
{
|
||||
getBoxAlgorithmContext()->markAlgorithmAsReadyToProcess();
|
||||
return true;
|
||||
}
|
||||
|
||||
namespace {
|
||||
typedef struct
|
||||
{
|
||||
uint64_t startTime;
|
||||
uint64_t endTime;
|
||||
CMatrix* matrix;
|
||||
} chunk_t;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmXDAWNSpatialFilterTrainer::process()
|
||||
{
|
||||
Kernel::IBoxIO& boxContext = this->getDynamicBoxContext();
|
||||
|
||||
bool shouldTrain = false;
|
||||
uint64_t date = 0, chunkStartTime = 0, chunkEndTime = 0;
|
||||
|
||||
for (size_t i = 0; i < boxContext.getInputChunkCount(0); ++i)
|
||||
{
|
||||
m_stimulationDecoder.decode(i);
|
||||
if (m_stimulationDecoder.isHeaderReceived())
|
||||
{
|
||||
m_encoder.encodeHeader();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
if (m_stimulationDecoder.isBufferReceived())
|
||||
{
|
||||
IStimulationSet* stimSet = m_stimulationDecoder.getOutputStimulationSet();
|
||||
// See if there is a training stimulation. If several, accept the first one.
|
||||
for (size_t j = 0; j < stimSet->getStimulationCount(); ++j)
|
||||
{
|
||||
if (stimSet->getStimulationIdentifier(j) == m_stimID)
|
||||
{
|
||||
date = stimSet->getStimulationDate(j); // date of the last matching stimulus in the set
|
||||
chunkStartTime = boxContext.getInputChunkStartTime(0, i);
|
||||
chunkEndTime = boxContext.getInputChunkEndTime(0, i);
|
||||
shouldTrain = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
if (m_stimulationDecoder.isEndReceived())
|
||||
{
|
||||
m_encoder.encodeEnd();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
boxContext.markInputAsDeprecated(0, i);
|
||||
}
|
||||
|
||||
if (shouldTrain)
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Info << "Received train stimulation - be patient\n";
|
||||
this->getLogManager() << Kernel::LogLevel_Trace << "Decoding signal chunks ...\n";
|
||||
|
||||
bool isContinuous = true;
|
||||
uint64_t end = 0;
|
||||
std::vector<chunk_t> chunks;
|
||||
for (size_t i = 0; i < boxContext.getInputChunkCount(1); ++i)
|
||||
{
|
||||
m_signalDecoder.decode(i);
|
||||
if (m_signalDecoder.isHeaderReceived())
|
||||
{
|
||||
// Don't care about the header
|
||||
}
|
||||
if (m_signalDecoder.isBufferReceived())
|
||||
{
|
||||
const CMatrix* matrix = m_signalDecoder.getOutputMatrix();
|
||||
chunk_t chunk;
|
||||
chunk.startTime = boxContext.getInputChunkStartTime(1, i);
|
||||
chunk.endTime = boxContext.getInputChunkEndTime(1, i);
|
||||
chunk.matrix = new CMatrix;
|
||||
chunk.matrix->copy(*matrix);
|
||||
chunks.push_back(chunk);
|
||||
|
||||
if (chunk.startTime != end)
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "Chunk " << i << " start time != last chunk end time [" << chunk.startTime << " vs "
|
||||
<< end << "]\n";
|
||||
isContinuous = false;
|
||||
break;
|
||||
}
|
||||
end = chunk.endTime;
|
||||
}
|
||||
if (m_signalDecoder.isEndReceived()) { }
|
||||
boxContext.markInputAsDeprecated(1, i);
|
||||
}
|
||||
|
||||
if (!isContinuous)
|
||||
{
|
||||
// @fixme mem leak
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "Input signal is not continuous... Can't continue\n";
|
||||
return false;
|
||||
}
|
||||
|
||||
this->getLogManager() << Kernel::LogLevel_Trace << "Decoding evoked response potential chunks ...\n";
|
||||
|
||||
std::vector<chunk_t> evokedPotential;
|
||||
for (size_t i = 0; i < boxContext.getInputChunkCount(2); ++i)
|
||||
{
|
||||
m_evokedPotentialDecoder.decode(i);
|
||||
if (m_evokedPotentialDecoder.isHeaderReceived())
|
||||
{
|
||||
// Don't care about the header
|
||||
}
|
||||
if (m_evokedPotentialDecoder.isBufferReceived())
|
||||
{
|
||||
const CMatrix* matrix = m_evokedPotentialDecoder.getOutputMatrix();
|
||||
chunk_t chunk;
|
||||
chunk.startTime = boxContext.getInputChunkStartTime(2, i);
|
||||
chunk.endTime = boxContext.getInputChunkEndTime(2, i);
|
||||
chunk.matrix = new CMatrix;
|
||||
chunk.matrix->copy(*matrix);
|
||||
evokedPotential.push_back(chunk);
|
||||
}
|
||||
if (m_evokedPotentialDecoder.isEndReceived()) { }
|
||||
boxContext.markInputAsDeprecated(2, i);
|
||||
}
|
||||
|
||||
if (evokedPotential.empty())
|
||||
{
|
||||
// @fixme mem leak
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "No evoked potentials received... Can't continue\n";
|
||||
return false;
|
||||
}
|
||||
|
||||
this->getLogManager() << Kernel::LogLevel_Trace << "Averaging evoked response potential...\n";
|
||||
|
||||
auto it = evokedPotential.begin();
|
||||
CMatrix averagedERPMatrixOV;
|
||||
averagedERPMatrixOV.copy(*it->matrix);
|
||||
for (++it; it != evokedPotential.end(); ++it)
|
||||
{
|
||||
const double* potentialBuffer = it->matrix->getBuffer();
|
||||
double* buffer = averagedERPMatrixOV.getBuffer();
|
||||
for (size_t j = 0; j < averagedERPMatrixOV.getBufferElementCount(); ++j) { *(buffer++) += *(potentialBuffer++); }
|
||||
}
|
||||
double* buffer = averagedERPMatrixOV.getBuffer();
|
||||
for (size_t j = 0; j < averagedERPMatrixOV.getBufferElementCount(); ++j) { (*buffer++) /= evokedPotential.size(); }
|
||||
|
||||
// WARNING - OpenViBE matrices are transposed ITPP matrices !
|
||||
|
||||
this->getLogManager() << Kernel::LogLevel_Trace << "Converting OpenViBE matrices to IT++ matrices...\n";
|
||||
|
||||
const size_t nChunk = chunks.size();
|
||||
const size_t nChannel = chunks.begin()->matrix->getDimensionSize(0);
|
||||
const size_t nSamplePerChunk = chunks.begin()->matrix->getDimensionSize(1);
|
||||
const size_t nSamplePerErp = averagedERPMatrixOV.getDimensionSize(1);
|
||||
|
||||
itpp::mat matrix(nChannel, nChunk * nSamplePerChunk);
|
||||
it = chunks.begin();
|
||||
for (size_t i = 0; it != chunks.end(); ++it, ++i)
|
||||
{
|
||||
const itpp::mat m = itppext::convert(*it->matrix);
|
||||
matrix.set_submatrix(0, int(i) * nSamplePerChunk, m);
|
||||
}
|
||||
|
||||
itpp::mat averagedERPMatrix(nChannel, nSamplePerErp);
|
||||
averagedERPMatrix = itppext::convert(averagedERPMatrixOV);
|
||||
|
||||
itpp::mat dMatrix(nChunk * nSamplePerChunk, nSamplePerErp);
|
||||
dMatrix.clear();
|
||||
for (it = evokedPotential.begin(); it != evokedPotential.end(); ++it)
|
||||
{
|
||||
// Compute index of the sample corresponding to the start of the ERP
|
||||
const uint64_t erpStartTime = it->startTime;
|
||||
const size_t erpStartIndex = size_t(CTime(erpStartTime).toSampleCount(m_signalDecoder.getOutputSamplingRate()));
|
||||
|
||||
for (size_t k = 0; k < nSamplePerErp; ++k) { dMatrix(erpStartIndex + k, k) = 1; }
|
||||
}
|
||||
|
||||
const itpp::mat A = (averagedERPMatrix * inv(dMatrix.transpose() * dMatrix) * averagedERPMatrix.transpose()) * double(evokedPotential.size())
|
||||
/ double(nSamplePerChunk * nChunk);
|
||||
std::stringstream s4;
|
||||
s4 << "A :\n" << A << "\n";
|
||||
this->getLogManager() << Kernel::LogLevel_Debug << s4.str() << "\n";
|
||||
|
||||
const itpp::mat B = (matrix * matrix.transpose()) / double(nSamplePerChunk * nChunk);
|
||||
|
||||
std::stringstream s5;
|
||||
s5 << "B :\n" << B << "\n";
|
||||
this->getLogManager() << Kernel::LogLevel_Debug << s5.str() << "\n";
|
||||
|
||||
// Free resources
|
||||
for (auto& c : chunks) { delete c.matrix; }
|
||||
chunks.clear();
|
||||
|
||||
for (auto& ep : evokedPotential) { delete ep.matrix; }
|
||||
evokedPotential.clear();
|
||||
|
||||
this->getLogManager() << Kernel::LogLevel_Trace << "Computing generalized eigen vector decomposition...\n";
|
||||
|
||||
itpp::mat eigenVector;
|
||||
itpp::vec eigenValue;
|
||||
|
||||
if (itppext::eig(A, B, eigenValue, eigenVector))
|
||||
{
|
||||
std::map<double, itpp::vec> eigenVectors;
|
||||
for (size_t i = 0; i < nChannel; ++i)
|
||||
{
|
||||
itpp::vec v = eigenVector.get_col(i);
|
||||
eigenVectors[eigenValue[i]] = v / norm(v);
|
||||
}
|
||||
|
||||
size_t cnt = 0;
|
||||
//We need to compute the size of the first dimension before setting the matrix
|
||||
const size_t dimension1Size = eigenVectors.size() < m_filterDim ? eigenVectors.size() : m_filterDim;
|
||||
|
||||
CMatrix outputVectors;
|
||||
outputVectors.resize(dimension1Size, nChannel);
|
||||
|
||||
auto itR = eigenVectors.rbegin();
|
||||
for (size_t i = 0; i < dimension1Size; ++itR, i++) { for (size_t j = 0; j < nChannel; ++j) { outputVectors.getBuffer()[cnt++] = itR->second[j]; } }
|
||||
if (m_saveAsBoxConfig)
|
||||
{
|
||||
FILE* file = fopen(m_spatialFilterConfigurationFilename.toASCIIString(), "wb");
|
||||
if (!file)
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "The file [" << m_spatialFilterConfigurationFilename
|
||||
<< "] could not be opened for writing...";
|
||||
return false;
|
||||
}
|
||||
|
||||
fprintf(file, "<OpenViBE-SettingsOverride>\n");
|
||||
fprintf(file, "\t<SettingValue>");
|
||||
for (size_t i = 0; i < outputVectors.getBufferElementCount(); ++i) { fprintf(file, "%e ", outputVectors.getBuffer()[i]); }
|
||||
fprintf(file, "</SettingValue>\n");
|
||||
fprintf(file, "\t<SettingValue>%zu</SettingValue>\n", m_filterDim);
|
||||
fprintf(file, "\t<SettingValue>%zu</SettingValue>\n", nChannel);
|
||||
fprintf(file, "\t<SettingValue></SettingValue>\n");
|
||||
fprintf(file, "</OpenViBE-SettingsOverride>\n");
|
||||
fclose(file);
|
||||
}
|
||||
else
|
||||
{
|
||||
if (!Toolkit::Matrix::saveToTextFile(outputVectors, m_spatialFilterConfigurationFilename))
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "Unable to save to [" << m_spatialFilterConfigurationFilename << "\n";
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
this->getLogManager() << Kernel::LogLevel_Info << "Training finished... Eigen values are ";
|
||||
itR = eigenVectors.rbegin();
|
||||
for (size_t i = 0; itR != eigenVectors.rend() && i < m_filterDim; ++itR, i++) { this->getLogManager() << " | " << double(itR->first); }
|
||||
this->getLogManager() << "\n";
|
||||
}
|
||||
else
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "Generalized eigen vector decomposition failed...\n";
|
||||
return false;
|
||||
}
|
||||
|
||||
this->getLogManager() << Kernel::LogLevel_Info << "xDAWN Spatial filter trained successfully.\n";
|
||||
|
||||
m_encoder.getInputStimulationSet()->clear();
|
||||
m_encoder.getInputStimulationSet()->appendStimulation(OVTK_StimulationId_TrainCompleted, date, 0);
|
||||
m_encoder.encodeBuffer();
|
||||
|
||||
boxContext.markOutputAsReadyToSend(0, chunkStartTime, chunkEndTime);
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
#endif // TARGET_HAS_ThirdPartyITPP
|
||||
+85
@@ -0,0 +1,85 @@
|
||||
#pragma once
|
||||
|
||||
#if defined TARGET_HAS_ThirdPartyITPP
|
||||
|
||||
#include "../../ovp_defines.h"
|
||||
#include <openvibe/ov_all.h>
|
||||
#include <toolkit/ovtk_all.h>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
class CBoxAlgorithmXDAWNSpatialFilterTrainer final : public Toolkit::TBoxAlgorithm<IBoxAlgorithm>
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { delete this; }
|
||||
|
||||
bool initialize() override;
|
||||
bool uninitialize() override;
|
||||
bool processInput(const size_t index) override;
|
||||
bool process() override;
|
||||
|
||||
_IsDerivedFromClass_Final_(Toolkit::TBoxAlgorithm<IBoxAlgorithm>, OVP_ClassId_BoxAlgorithm_XDAWNSpatialFilterTrainer)
|
||||
|
||||
protected:
|
||||
|
||||
Toolkit::TStimulationDecoder<CBoxAlgorithmXDAWNSpatialFilterTrainer> m_stimulationDecoder;
|
||||
Toolkit::TSignalDecoder<CBoxAlgorithmXDAWNSpatialFilterTrainer> m_signalDecoder;
|
||||
Toolkit::TSignalDecoder<CBoxAlgorithmXDAWNSpatialFilterTrainer> m_evokedPotentialDecoder;
|
||||
|
||||
Toolkit::TStimulationEncoder<CBoxAlgorithmXDAWNSpatialFilterTrainer> m_encoder;
|
||||
|
||||
uint64_t m_stimID = 0;
|
||||
CString m_spatialFilterConfigurationFilename;
|
||||
size_t m_filterDim = 0;
|
||||
bool m_saveAsBoxConfig = false;
|
||||
};
|
||||
|
||||
class CBoxAlgorithmXDAWNSpatialFilterTrainerDesc final : public IBoxAlgorithmDesc
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { }
|
||||
|
||||
CString getName() const override { return CString("xDAWN Trainer (Deprecated)"); }
|
||||
CString getAuthorName() const override { return CString("Yann Renard"); }
|
||||
CString getAuthorCompanyName() const override { return CString("INRIA"); }
|
||||
|
||||
CString getShortDescription() const override
|
||||
{
|
||||
return CString(
|
||||
"Computes spatial filter coeffcients in order to get better evoked potential classification (typically used for P300 detection)");
|
||||
}
|
||||
|
||||
CString getDetailedDescription() const override { return CString(""); }
|
||||
CString getCategory() const override { return CString("Signal processing/Filtering"); }
|
||||
CString getVersion() const override { return CString("1.0"); }
|
||||
CString getStockItemName() const override { return CString("gtk-missing-image"); }
|
||||
|
||||
CIdentifier getCreatedClass() const override { return OVP_ClassId_BoxAlgorithm_XDAWNSpatialFilterTrainer; }
|
||||
IPluginObject* create() override { return new CBoxAlgorithmXDAWNSpatialFilterTrainer; }
|
||||
|
||||
bool getBoxPrototype(Kernel::IBoxProto& prototype) const override
|
||||
{
|
||||
prototype.addInput("Stimulations", OV_TypeId_Stimulations);
|
||||
prototype.addInput("Session signal", OV_TypeId_Signal);
|
||||
prototype.addInput("Evoked potential epochs", OV_TypeId_Signal);
|
||||
prototype.addOutput("Train-completed Flag", OV_TypeId_Stimulations);
|
||||
|
||||
prototype.addSetting("Train stimulation", OV_TypeId_Stimulation, "OVTK_StimulationId_Train");
|
||||
prototype.addSetting("Spatial filter configuration", OV_TypeId_Filename, "");
|
||||
prototype.addSetting("Filter dimension", OV_TypeId_Integer, "4");
|
||||
prototype.addSetting("Save as box config", OV_TypeId_Boolean, "true");
|
||||
// prototype.addFlag(OV_AttributeId_Box_FlagIsUnstable);
|
||||
prototype.addFlag(Kernel::BoxFlag_IsDeprecated);
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(IBoxAlgorithmDesc, OVP_ClassId_BoxAlgorithm_XDAWNSpatialFilterTrainerDesc)
|
||||
};
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
|
||||
#endif // TARGET_HAS_ThirdPartyITPP
|
||||
+216
@@ -0,0 +1,216 @@
|
||||
#if defined(TARGET_HAS_ThirdPartyFFTW3) // fftw3 required by wavelet2s
|
||||
|
||||
#include "ovpCBoxAlgorithmDiscreteWaveletTransform.h"
|
||||
|
||||
#include <cstdlib>
|
||||
#include <vector>
|
||||
#include <map>
|
||||
#include <math.h>
|
||||
#include <fstream>
|
||||
#include <string>
|
||||
|
||||
#include "../../../contrib/packages/wavelet2d/wavelet2s.h"
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
bool CBoxAlgorithmDiscreteWaveletTransform::initialize()
|
||||
{
|
||||
const size_t nOutput = this->getStaticBoxContext().getOutputCount();
|
||||
m_decoder.initialize(*this, 0); // Signal stream decoder
|
||||
m_encoder.initialize(*this, 0); // Signal stream encoder
|
||||
|
||||
m_waveletType = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 0);
|
||||
m_decompositionLevel = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 1);
|
||||
|
||||
for (size_t o = 0; o < nOutput - 1; ++o) { m_encoders.push_back(new Toolkit::TSignalEncoder<CBoxAlgorithmDiscreteWaveletTransform>(*this, o + 1)); }
|
||||
|
||||
m_infolength = 0;
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
bool CBoxAlgorithmDiscreteWaveletTransform::uninitialize()
|
||||
{
|
||||
m_decoder.uninitialize();
|
||||
m_encoder.uninitialize();
|
||||
|
||||
for (auto& elem : m_encoders)
|
||||
{
|
||||
elem->uninitialize();
|
||||
delete elem;
|
||||
}
|
||||
m_encoders.clear();
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
bool CBoxAlgorithmDiscreteWaveletTransform::processInput(const size_t /*index*/)
|
||||
{
|
||||
getBoxAlgorithmContext()->markAlgorithmAsReadyToProcess();
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
bool CBoxAlgorithmDiscreteWaveletTransform::process()
|
||||
{
|
||||
// the dynamic box context describes the current state of the box inputs and outputs (i.e. the chunks)
|
||||
Kernel::IBoxIO& boxContext = this->getDynamicBoxContext();
|
||||
|
||||
const int j = std::atoi(m_decompositionLevel);
|
||||
const std::string nm(m_waveletType.toASCIIString());
|
||||
|
||||
for (size_t i = 0; i < boxContext.getInputChunkCount(0); ++i)
|
||||
{
|
||||
//Decode input signal
|
||||
m_decoder.decode(i);
|
||||
|
||||
// Construct header when we receive one
|
||||
if (m_decoder.isHeaderReceived())
|
||||
{
|
||||
const size_t nChannels0 = m_decoder.getOutputMatrix()->getDimensionSize(0);
|
||||
const size_t nSamples0 = m_decoder.getOutputMatrix()->getDimensionSize(1);
|
||||
|
||||
if (nSamples0 <= std::pow(2.0, j + 1))
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "Number of samples [" << nSamples0 << "] is smaller or equal than 2^{J+1} == ["
|
||||
<< std::pow(2.0, j + 1) << "]\n";
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "Verify quantity of samples and number of decomposition levels" << "\n";
|
||||
this->getLogManager() << Kernel::LogLevel_Error <<
|
||||
"You can introduce a Time based epoching to have more samples per chunk or reduce the decomposition levels" << "\n";
|
||||
return false;
|
||||
}
|
||||
|
||||
//sig will be resized to the number of channels and the total number of samples (Channels x Samples)
|
||||
m_sig.resize(nChannels0);
|
||||
for (size_t c = 0; c < nChannels0; ++c) { m_sig[c].resize(nSamples0); }
|
||||
|
||||
//Do one dummy transform to get the m_flag and m_length filled. Since all channels & blocks have the same chunk size in OV, once is enough.
|
||||
std::vector<double> flag; //flag is an auxiliar vector (see wavelet2d library)
|
||||
std::vector<size_t> length; //length contains the length of each decomposition level. last entry is the length of the original signal.
|
||||
std::vector<double> dwtOutput; //dwt_output is the vector containing the decomposition levels
|
||||
|
||||
dwt(m_sig[0], j, nm, dwtOutput, flag, length);
|
||||
|
||||
// Set info stream dimension
|
||||
m_infolength = (length.size() + flag.size() + 2);
|
||||
m_encoder.getInputMatrix()->resize(nChannels0, m_infolength);
|
||||
|
||||
// Set decomposition stream dimensions
|
||||
for (size_t e = 0; e < m_encoders.size(); ++e) { m_encoders[e]->getInputMatrix()->resize(nChannels0, length[e]); }
|
||||
|
||||
// Set decomposition stream channel names
|
||||
for (size_t c = 0; c < nChannels0; c++)
|
||||
{
|
||||
for (auto& encoder : m_encoders) { encoder->getInputMatrix()->setDimensionLabel(0, c, m_decoder.getOutputMatrix()->getDimensionLabel(0, c)); }
|
||||
}
|
||||
|
||||
|
||||
// Info stream header
|
||||
m_encoder.getInputSamplingRate().setReferenceTarget(m_decoder.getOutputSamplingRate());
|
||||
m_encoder.encodeHeader();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
|
||||
// Decomposition stream headers
|
||||
for (size_t e = 0; e < m_encoders.size(); ++e)
|
||||
{
|
||||
const double sampling = double(m_decoder.getOutputSamplingRate()) / std::pow(2.0, int(e));
|
||||
m_encoders[e]->getInputSamplingRate() = uint64_t(std::floor(sampling));
|
||||
|
||||
m_encoders[e]->encodeHeader();
|
||||
boxContext.markOutputAsReadyToSend(e + 1, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
}
|
||||
|
||||
if (m_decoder.isBufferReceived())
|
||||
{
|
||||
const CMatrix* matrix = m_decoder.getOutputMatrix();
|
||||
const double* buffer0 = matrix->getBuffer();
|
||||
|
||||
const size_t nChannels0 = matrix->getDimensionSize(0);
|
||||
const size_t nSamples0 = matrix->getDimensionSize(1);
|
||||
|
||||
//sig will store the samples of the different channels
|
||||
for (size_t c = 0; c < nChannels0; ++c) //Number of EEG channels
|
||||
{
|
||||
for (size_t s = 0; s < nSamples0; ++s) //Number of Samples per Chunk
|
||||
{
|
||||
m_sig[c][s] = (buffer0[s + c * nSamples0]);
|
||||
}
|
||||
}
|
||||
|
||||
// Due to how wavelet2s works, we'll have to have the output variables empty before each call.
|
||||
std::vector<std::vector<double>> flag;
|
||||
std::vector<std::vector<size_t>> length;
|
||||
std::vector<std::vector<double>> dwtOutput;
|
||||
flag.resize(nChannels0);
|
||||
length.resize(nChannels0);
|
||||
dwtOutput.resize(nChannels0);
|
||||
|
||||
//Calculation of wavelets coefficients for each channel.
|
||||
for (size_t c = 0; c < nChannels0; ++c) { dwt(m_sig[c], j, nm, dwtOutput[c], flag[c], length[c]); }
|
||||
|
||||
//Transmission of some information (flag and legth) to the inverse dwt box
|
||||
//@fixme since the data dimensions do not change runtime, it should be sufficient to send this only once
|
||||
for (size_t c = 0; c < nChannels0; ++c)
|
||||
{
|
||||
size_t f = 0;
|
||||
m_encoder.getInputMatrix()->getBuffer()[f + c * m_infolength] = double(length[c].size());
|
||||
for (size_t l = 0; l < length[c].size(); ++l)
|
||||
{
|
||||
m_encoder.getInputMatrix()->getBuffer()[l + 1 + c * m_infolength] = double(length[c][l]);
|
||||
f = l;
|
||||
}
|
||||
m_encoder.getInputMatrix()->getBuffer()[f + 2 + c * m_infolength] = double(flag[c].size());
|
||||
for (size_t l = 0; l < flag[c].size(); ++l) { m_encoder.getInputMatrix()->getBuffer()[f + 3 + l + c * m_infolength] = flag[c][l]; }
|
||||
}
|
||||
|
||||
//Decode the dwt coefficients of each decomposition level to separate channels
|
||||
for (size_t c = 0; c < nChannels0; ++c)
|
||||
{
|
||||
for (size_t e = 0, vectorPos = 0; e < m_encoders.size(); ++e)
|
||||
{
|
||||
CMatrix* oMatrix = m_encoders[e]->getInputMatrix();
|
||||
double* oBuffer = oMatrix->getBuffer();
|
||||
|
||||
// loop levels
|
||||
for (size_t l = 0; l < size_t(length[c][e]); ++l) { oBuffer[l + c * length[c][e]] = dwtOutput[c][l + vectorPos]; }
|
||||
|
||||
vectorPos = vectorPos + length[c][e];
|
||||
}
|
||||
}
|
||||
|
||||
m_encoder.encodeBuffer();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
|
||||
for (size_t e = 0; e < m_encoders.size(); ++e)
|
||||
{
|
||||
m_encoders[e]->encodeBuffer();
|
||||
boxContext.markOutputAsReadyToSend(e + 1, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
}
|
||||
|
||||
if (m_decoder.isEndReceived())
|
||||
{
|
||||
m_encoder.encodeEnd();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
|
||||
for (size_t e = 0; e < m_encoders.size(); ++e)
|
||||
{
|
||||
m_encoders[e]->encodeEnd();
|
||||
boxContext.markOutputAsReadyToSend(e + 1, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
#endif
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+141
@@ -0,0 +1,141 @@
|
||||
#pragma once
|
||||
|
||||
#if defined(TARGET_HAS_ThirdPartyFFTW3) // required by wavelet2s
|
||||
|
||||
//You may have to change this path to match your folder organisation
|
||||
#include "../ovp_defines.h"
|
||||
|
||||
#include <openvibe/ov_all.h>
|
||||
#include <toolkit/ovtk_all.h>
|
||||
#include <iostream>
|
||||
#include <string>
|
||||
#include <sstream>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
/**
|
||||
* \class CBoxAlgorithmDiscreteWaveletTransform
|
||||
* \author Joao-Pedro Berti-Ligabo / Inria
|
||||
* \date Wed Jul 16 15:05:16 2014
|
||||
* \brief The class CBoxAlgorithmDiscreteWaveletTransform describes the box DiscreteWaveletTransform.
|
||||
*
|
||||
*/
|
||||
class CBoxAlgorithmDiscreteWaveletTransform final : virtual public Toolkit::TBoxAlgorithm<IBoxAlgorithm>
|
||||
{
|
||||
public:
|
||||
void release() override { delete this; }
|
||||
|
||||
bool initialize() override;
|
||||
bool uninitialize() override;
|
||||
|
||||
bool processInput(const size_t index) override;
|
||||
bool process() override;
|
||||
|
||||
_IsDerivedFromClass_Final_(Toolkit::TBoxAlgorithm<IBoxAlgorithm>, OVP_ClassId_BoxAlgorithm_DiscreteWaveletTransform)
|
||||
|
||||
protected:
|
||||
// Codec algorithms specified in the skeleton-generator:
|
||||
// Signal stream decoder
|
||||
Toolkit::TSignalDecoder<CBoxAlgorithmDiscreteWaveletTransform> m_decoder;
|
||||
|
||||
Toolkit::TSignalEncoder<CBoxAlgorithmDiscreteWaveletTransform> m_encoder;
|
||||
std::vector<Toolkit::TSignalEncoder<CBoxAlgorithmDiscreteWaveletTransform>*> m_encoders;
|
||||
|
||||
CString m_waveletType;
|
||||
CString m_decompositionLevel;
|
||||
|
||||
size_t m_infolength = 0;
|
||||
std::vector<std::vector<double>> m_sig;
|
||||
};
|
||||
|
||||
|
||||
// The box listener can be used to call specific callbacks whenever the box structure changes : input added, name changed, etc.
|
||||
// Please uncomment below the callbacks you want to use.
|
||||
class CBoxAlgorithmDiscreteWaveletTransformListener final : public Toolkit::TBoxListener<IBoxListener>
|
||||
{
|
||||
public:
|
||||
|
||||
bool onSettingValueChanged(Kernel::IBox& box, const size_t index) override
|
||||
{
|
||||
if (index == 0) { return true; }
|
||||
|
||||
if (index == 1)
|
||||
{
|
||||
const size_t nOutputs = box.getOutputCount();
|
||||
CString str;
|
||||
box.getSettingValue(1, str);
|
||||
const size_t nDecompositionLevels = atoi(str);
|
||||
if (nOutputs != nDecompositionLevels + 2)
|
||||
{
|
||||
for (size_t i = 0; i < nOutputs; ++i) { box.removeOutput(nOutputs - i - 1); }
|
||||
|
||||
box.addOutput("Info",OV_TypeId_Signal);
|
||||
box.addOutput("A",OV_TypeId_Signal);
|
||||
for (size_t i = nDecompositionLevels; i > 0; i--) { box.addOutput(("D" + std::to_string(i)).c_str(),OV_TypeId_Signal); }
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(Toolkit::TBoxListener<IBoxListener>, CIdentifier::undefined())
|
||||
};
|
||||
|
||||
|
||||
/**
|
||||
* \class CBoxAlgorithmDiscreteWaveletTransformDesc
|
||||
* \author Joao-Pedro Berti-Ligabo / Inria
|
||||
* \date Wed Jul 16 15:05:16 2014
|
||||
* \brief Descriptor of the box DiscreteWaveletTransform.
|
||||
*
|
||||
*/
|
||||
class CBoxAlgorithmDiscreteWaveletTransformDesc final : virtual public IBoxAlgorithmDesc
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { }
|
||||
|
||||
CString getName() const override { return CString("Discrete Wavelet Transform"); }
|
||||
CString getAuthorName() const override { return CString("Joao-Pedro Berti-Ligabo"); }
|
||||
CString getAuthorCompanyName() const override { return CString("Inria"); }
|
||||
CString getShortDescription() const override { return CString("Calculate DiscreteWaveletTransform"); }
|
||||
|
||||
CString getDetailedDescription() const override { return CString("Calculate DiscreteWaveletTransform using different types of wavelets"); }
|
||||
|
||||
CString getCategory() const override { return CString("Signal processing/Wavelets"); }
|
||||
CString getVersion() const override { return CString("1.0"); }
|
||||
CString getStockItemName() const override { return CString("gnome-fs-regular.png"); }
|
||||
|
||||
CIdentifier getCreatedClass() const override { return OVP_ClassId_BoxAlgorithm_DiscreteWaveletTransform; }
|
||||
IPluginObject* create() override { return new CBoxAlgorithmDiscreteWaveletTransform; }
|
||||
|
||||
|
||||
IBoxListener* createBoxListener() const override { return new CBoxAlgorithmDiscreteWaveletTransformListener; }
|
||||
void releaseBoxListener(IBoxListener* listener) const override { delete listener; }
|
||||
|
||||
bool getBoxPrototype(Kernel::IBoxProto& prototype) const override
|
||||
{
|
||||
prototype.addInput("Signal",OV_TypeId_Signal);
|
||||
|
||||
prototype.addOutput("Info",OV_TypeId_Signal);
|
||||
prototype.addOutput("A",OV_TypeId_Signal);
|
||||
prototype.addOutput("D2",OV_TypeId_Signal);
|
||||
prototype.addOutput("D1",OV_TypeId_Signal);
|
||||
|
||||
prototype.addSetting("Wavelet type",OVP_TypeId_WaveletType, "");
|
||||
prototype.addSetting("Wavelet decomposition levels",OVP_TypeId_WaveletLevel, "");
|
||||
|
||||
prototype.addFlag(OV_AttributeId_Box_FlagIsUnstable);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(IBoxAlgorithmDesc, OVP_ClassId_BoxAlgorithm_DiscreteWaveletTransformDesc)
|
||||
};
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
|
||||
|
||||
#endif
|
||||
+172
@@ -0,0 +1,172 @@
|
||||
#if defined(TARGET_HAS_ThirdPartyEIGEN)
|
||||
|
||||
#include "ovpCBoxAlgorithmEOG_Denoising.h"
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
bool CBoxAlgorithmEOG_Denoising::initialize()
|
||||
{
|
||||
// Signal stream decoder
|
||||
m_algo0SignalDecoder.initialize(*this, 0);
|
||||
m_algo1SignalDecoder.initialize(*this, 1);
|
||||
|
||||
m_algo2SignalEncoder.getInputSamplingRate().setReferenceTarget(m_algo0SignalDecoder.getOutputSamplingRate());
|
||||
|
||||
m_filename = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 0);
|
||||
|
||||
m_fBMatrixFile.open(m_filename.toASCIIString(), std::ios::in);
|
||||
if (m_fBMatrixFile.fail())
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "Failed to open [" << m_filename << "] for reading\n";
|
||||
return false;
|
||||
}
|
||||
|
||||
m_fBMatrixFile >> m_nChannels0;
|
||||
m_fBMatrixFile >> m_nChannels1;
|
||||
m_fBMatrixFile >> m_nSamples0;
|
||||
|
||||
if (m_fBMatrixFile.fail())
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "Not able to successfully read dims from [" << m_filename << "]\n";
|
||||
m_fBMatrixFile.close();
|
||||
return false;
|
||||
}
|
||||
|
||||
|
||||
m_nSamples1 = m_nSamples0;
|
||||
|
||||
m_noiseCoeff.resize(m_nChannels0, m_nChannels1); //Noise Coefficients Matrix (Dim: Channels EEG x Channels EOG)
|
||||
m_noiseCoeff.setZero(m_nChannels0, m_nChannels1);
|
||||
|
||||
for (size_t i = 0; i < m_nChannels0; ++i) //Number of channels
|
||||
{
|
||||
for (size_t j = 0; j < m_nChannels1; ++j) { m_fBMatrixFile >> m_noiseCoeff(i, j); } //Number of Samples per Chunk
|
||||
}
|
||||
|
||||
if (m_fBMatrixFile.fail())
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "Not able to successfully read coefficients from [" << m_filename << "]\n";
|
||||
m_fBMatrixFile.close();
|
||||
return false;
|
||||
}
|
||||
|
||||
m_fBMatrixFile.close();
|
||||
|
||||
// Signal stream encoder
|
||||
m_algo2SignalEncoder.initialize(*this, 0);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
bool CBoxAlgorithmEOG_Denoising::uninitialize()
|
||||
{
|
||||
m_algo0SignalDecoder.uninitialize();
|
||||
m_algo1SignalDecoder.uninitialize();
|
||||
m_algo2SignalEncoder.uninitialize();
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
bool CBoxAlgorithmEOG_Denoising::processInput(const size_t /*index*/)
|
||||
{
|
||||
getBoxAlgorithmContext()->markAlgorithmAsReadyToProcess();
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
bool CBoxAlgorithmEOG_Denoising::process()
|
||||
{
|
||||
Kernel::IBoxIO& boxContext = this->getDynamicBoxContext();
|
||||
|
||||
Eigen::MatrixXd data0(m_nChannels0, m_nSamples0); //EEG data
|
||||
Eigen::MatrixXd data1(m_nChannels1, m_nSamples1); //EOG data
|
||||
Eigen::MatrixXd eegC(m_nChannels0, m_nSamples0); //Corrected Matrix
|
||||
|
||||
if (boxContext.getInputChunkCount(0) != 0)
|
||||
{
|
||||
for (size_t i = 0; i < boxContext.getInputChunkCount(1); ++i) //Don't know why getinputchunkcount(1)
|
||||
{
|
||||
// Signal EEG
|
||||
|
||||
// decode the chunk ii on input 0
|
||||
m_algo0SignalDecoder.decode(i);
|
||||
|
||||
CMatrix* matrix0 = m_algo0SignalDecoder.getOutputMatrix();
|
||||
double* buffer0 = matrix0->getBuffer();
|
||||
|
||||
for (size_t c = 0; c < m_nChannels0; ++c) //Number of channels
|
||||
{
|
||||
for (size_t s = 0; s < m_nSamples0; ++s) { data0(c, s) = buffer0[s + c * m_nSamples0]; } //Number of Samples per Chunk
|
||||
}
|
||||
|
||||
//Signal EOG
|
||||
m_algo1SignalDecoder.decode(i);
|
||||
|
||||
CMatrix* matrix1 = m_algo1SignalDecoder.getOutputMatrix();
|
||||
double* buffer1 = matrix1->getBuffer();
|
||||
|
||||
for (size_t c = 0; c < m_nChannels1; ++c) //Number of channels
|
||||
{
|
||||
for (size_t s = 0; s < m_nSamples1; ++s) { data1(c, s) = buffer1[s + c * m_nSamples1]; } //Number of Samples per Chunk
|
||||
}
|
||||
|
||||
|
||||
//Set the output (corrected EEG) to the same structure as the EEG input
|
||||
m_algo2SignalEncoder.getInputMatrix()->resize(m_nChannels0, m_nSamples0);
|
||||
|
||||
|
||||
for (size_t c = 0; c < m_nChannels0; c++)
|
||||
{
|
||||
m_algo2SignalEncoder.getInputMatrix()->setDimensionLabel(0, c, m_algo0SignalDecoder.getOutputMatrix()->getDimensionLabel(0, c));
|
||||
}
|
||||
|
||||
|
||||
//Remove the noise
|
||||
eegC = data0 - (m_noiseCoeff * data1);
|
||||
|
||||
|
||||
for (size_t c = 0; c < m_nChannels0; ++c) //Number of EEG channels
|
||||
{
|
||||
for (size_t s = 0; s < m_nSamples0; ++s) //Number of Samples per Chunk
|
||||
{
|
||||
m_algo2SignalEncoder.getInputMatrix()->getBuffer()[s + c * m_nSamples0] = eegC(c, s);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
m_algo2SignalEncoder.getInputSamplingRate().setReferenceTarget(m_algo1SignalDecoder.getOutputSamplingRate());
|
||||
|
||||
|
||||
if (m_algo1SignalDecoder.isHeaderReceived())
|
||||
{
|
||||
m_algo2SignalEncoder.encodeHeader();
|
||||
// send the output chunk containing the header. The dates are the same as the input chunk:
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
|
||||
if (m_algo1SignalDecoder.isBufferReceived())
|
||||
{
|
||||
// Encode the output buffer :
|
||||
m_algo2SignalEncoder.encodeBuffer();
|
||||
// and send it to the next boxes :
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
if (m_algo1SignalDecoder.isEndReceived())
|
||||
{
|
||||
// End of stream received. This happens only once when pressing "stop". Just pass it to the next boxes so they receive the message :
|
||||
m_algo2SignalEncoder.encodeEnd();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
#endif
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+125
@@ -0,0 +1,125 @@
|
||||
#pragma once
|
||||
|
||||
#if defined(TARGET_HAS_ThirdPartyEIGEN)
|
||||
|
||||
//You may have to change this path to match your folder organisation
|
||||
#include "../ovp_defines.h"
|
||||
|
||||
#include <openvibe/ov_all.h>
|
||||
#include <toolkit/ovtk_all.h>
|
||||
#include <fstream>
|
||||
|
||||
// Verify Eigen Path
|
||||
#include <Eigen/Dense>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
/**
|
||||
* \class CBoxAlgorithmEOG_Denoising
|
||||
* \author Joao-Pedro Berti-Ligabo / Inria
|
||||
* \date Tue May 20 15:33:22 2014
|
||||
* \brief The class CBoxAlgorithmEOG_Denoising describes the box Test.
|
||||
*
|
||||
*/
|
||||
class CBoxAlgorithmEOG_Denoising final : virtual public Toolkit::TBoxAlgorithm<IBoxAlgorithm>
|
||||
{
|
||||
public:
|
||||
void release() override { delete this; }
|
||||
|
||||
bool initialize() override;
|
||||
bool uninitialize() override;
|
||||
|
||||
//Here is the different process callbacks possible
|
||||
// - On clock ticks :
|
||||
//virtual bool processClock(Kernel::CMessageClock& msg);
|
||||
// - On new input received (the most common behaviour for signal processing) :
|
||||
bool processInput(const size_t index) override;
|
||||
|
||||
// If you want to use processClock, you must provide the clock frequency.
|
||||
//virtual uint64_t getClockFrequency();
|
||||
|
||||
bool process() override;
|
||||
|
||||
// As we do with any class in openvibe, we use the macro below
|
||||
// to associate this box to an unique identifier.
|
||||
// The inheritance information is also made available,
|
||||
// as we provide the superclass Toolkit::TBoxAlgorithm < IBoxAlgorithm >
|
||||
_IsDerivedFromClass_Final_(Toolkit::TBoxAlgorithm<IBoxAlgorithm>, OVP_ClassId_BoxAlgorithm_EOG_Denoising)
|
||||
|
||||
protected:
|
||||
// Codec algorithms specified in the skeleton-generator:
|
||||
// Signal stream decoder
|
||||
Toolkit::TSignalDecoder<CBoxAlgorithmEOG_Denoising> m_algo0SignalDecoder;
|
||||
Toolkit::TSignalDecoder<CBoxAlgorithmEOG_Denoising> m_algo1SignalDecoder;
|
||||
|
||||
// Kernel::IAlgorithmProxy* m_matrixRegressionAlgorithm;
|
||||
// Kernel::TParameterHandler < CMatrix* > ip_pMatrixRegressionAlgorithm_Matrix0;
|
||||
// Kernel::TParameterHandler < CMatrix* > ip_pMatrixRegressionAlgorithm_Matrix1;
|
||||
|
||||
// Kernel::TParameterHandler < CMatrix* > op_pMatrixRegressionAlgorithm_Matrix;
|
||||
// Kernel::TParameterHandler < CString > par_Filename;
|
||||
|
||||
|
||||
// Signal stream encoder
|
||||
Toolkit::TSignalEncoder<CBoxAlgorithmEOG_Denoising> m_algo2SignalEncoder;
|
||||
|
||||
CString m_filename;
|
||||
std::ifstream m_fBMatrixFile;
|
||||
Eigen::MatrixXd m_noiseCoeff;
|
||||
|
||||
size_t m_nChannels0 = 0;
|
||||
size_t m_nChannels1 = 0;
|
||||
|
||||
size_t m_nSamples0 = 0;
|
||||
size_t m_nSamples1 = 0;
|
||||
};
|
||||
|
||||
|
||||
/**
|
||||
* \class CBoxAlgorithmEOG_DenoisingDesc
|
||||
* \author Joao-Pedro Berti-Ligabo / Inria
|
||||
* \date Tue May 20 15:33:22 2014
|
||||
* \brief Descriptor of the box Test.
|
||||
*
|
||||
*/
|
||||
class CBoxAlgorithmEOG_DenoisingDesc final : virtual public IBoxAlgorithmDesc
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { }
|
||||
|
||||
CString getName() const override { return CString("EOG Denoising"); }
|
||||
CString getAuthorName() const override { return CString("Joao-Pedro Berti-Ligabo"); }
|
||||
CString getAuthorCompanyName() const override { return CString("Inria"); }
|
||||
CString getShortDescription() const override { return CString("EOG Denoising using Regression Analysis"); }
|
||||
|
||||
CString getDetailedDescription() const override { return CString("Algorithm implementation as suggested in Schlogl's article of 2007"); }
|
||||
|
||||
CString getCategory() const override { return CString("Signal processing/Denoising"); }
|
||||
CString getVersion() const override { return CString("023"); }
|
||||
CString getStockItemName() const override { return CString("gnome-fs-regular.png"); }
|
||||
|
||||
CIdentifier getCreatedClass() const override { return OVP_ClassId_BoxAlgorithm_EOG_Denoising; }
|
||||
IPluginObject* create() override { return new CBoxAlgorithmEOG_Denoising; }
|
||||
|
||||
bool getBoxPrototype(Kernel::IBoxProto& prototype) const override
|
||||
{
|
||||
prototype.addInput("EEG",OV_TypeId_Signal);
|
||||
prototype.addInput("EOG",OV_TypeId_Signal);
|
||||
|
||||
prototype.addOutput("EEG_Corrected",OV_TypeId_Signal);
|
||||
prototype.addSetting("Filename b Matrix", OV_TypeId_Filename, "b-Matrix-EEG.txt");
|
||||
|
||||
prototype.addFlag(OV_AttributeId_Box_FlagIsUnstable);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(IBoxAlgorithmDesc, OVP_ClassId_BoxAlgorithm_EOG_DenoisingDesc)
|
||||
};
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
|
||||
#endif
|
||||
+386
@@ -0,0 +1,386 @@
|
||||
#if defined(TARGET_HAS_ThirdPartyEIGEN)
|
||||
|
||||
#include "ovpCBoxAlgorithmEOG_Denoising_Calibration.h"
|
||||
|
||||
#include <Eigen/Dense>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
bool CBoxAlgorithmEOG_Denoising_Calibration::initialize()
|
||||
{
|
||||
m_algo0SignalDecoder.initialize(*this, 0);
|
||||
m_algo1SignalDecoder.initialize(*this, 1);
|
||||
m_algo2StimulationDecoder.initialize(*this, 2);
|
||||
m_stimulationEncoder.initialize(*this, 0);
|
||||
|
||||
m_calibrationFilename = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 0);
|
||||
m_stimID = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 1);
|
||||
|
||||
m_startTime = 0;
|
||||
m_endTime = 0;
|
||||
m_nChunks = 0;
|
||||
m_chunksVerify = -1;
|
||||
m_endProcess = false;
|
||||
m_time = 0;
|
||||
m_startTimeChunks = 0;
|
||||
m_endTimeChunks = 0;
|
||||
|
||||
// Random id for tmp token, clash possible if multiple boxes run in parallel (but unlikely)
|
||||
const CString randomToken = CIdentifier::random().toString();
|
||||
m_eegTempFilename = this->getConfigurationManager().expand("${Path_Tmp}/denoising_") + randomToken + "_EEG_tmp.dat";
|
||||
m_eogTempFilename = this->getConfigurationManager().expand("${Path_Tmp}/denoising_") + randomToken + "_EOG_tmp.dat";
|
||||
|
||||
m_eegFile.open(m_eegTempFilename, std::ios::out | std::ios::in | std::ios::trunc);
|
||||
if (m_eegFile.fail())
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "Opening [" << m_eegTempFilename << "] for r/w failed\n";
|
||||
return false;
|
||||
}
|
||||
|
||||
m_eogFile.open(m_eogTempFilename, std::ios::out | std::ios::in | std::ios::trunc);
|
||||
if (m_eogFile.fail())
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "Opening [" << m_eogTempFilename << "] for r/w failed\n";
|
||||
return false;
|
||||
}
|
||||
|
||||
m_matrixFile.open(m_calibrationFilename.toASCIIString(), std::ios::out | std::ios::trunc);
|
||||
if (m_matrixFile.fail())
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "Opening [" << m_calibrationFilename << "] for writing failed\n";
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
bool CBoxAlgorithmEOG_Denoising_Calibration::uninitialize()
|
||||
{
|
||||
m_algo0SignalDecoder.uninitialize();
|
||||
m_algo1SignalDecoder.uninitialize();
|
||||
m_algo2StimulationDecoder.uninitialize();
|
||||
m_stimulationEncoder.uninitialize();
|
||||
|
||||
// Clean up temporary files
|
||||
if (m_eegFile.is_open()) { m_eegFile.close(); }
|
||||
if (m_eegTempFilename != CString("")) { std::remove(m_eegTempFilename); }
|
||||
|
||||
if (m_eogFile.is_open()) { m_eogFile.close(); }
|
||||
if (m_eogTempFilename != CString("")) { std::remove(m_eogTempFilename); }
|
||||
|
||||
if (m_matrixFile.is_open()) { m_matrixFile.close(); }
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
bool CBoxAlgorithmEOG_Denoising_Calibration::processClock(Kernel::CMessageClock& /*msg*/)
|
||||
{
|
||||
Kernel::IBoxIO& boxContext = this->getDynamicBoxContext();
|
||||
|
||||
if (m_nChunks != m_chunksVerify && m_endProcess == false)
|
||||
{
|
||||
m_chunksVerify = m_nChunks;
|
||||
if (m_time == m_startTime) { m_startTimeChunks = m_nChunks; }
|
||||
|
||||
if (m_time == m_endTime) { m_endTimeChunks = m_nChunks; }
|
||||
}
|
||||
else if (m_nChunks == m_chunksVerify && m_endProcess == false)
|
||||
{
|
||||
if ((m_startTime >= m_endTime) || (m_endTime >= m_time))
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Warning << "Verify time interval of sampling" << "\n";
|
||||
this->getLogManager() << Kernel::LogLevel_Warning << "Total time of your sample: " << m_time << "\n";
|
||||
this->getLogManager() << Kernel::LogLevel_Warning << "b Matrix was NOT successfully calculated" << "\n";
|
||||
|
||||
m_stimulationEncoder.getInputStimulationSet()->clear();
|
||||
m_stimulationEncoder.getInputStimulationSet()->appendStimulation(OVTK_StimulationId_TrainCompleted, 0, 0);
|
||||
m_stimulationEncoder.encodeBuffer();
|
||||
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, 0), boxContext.getInputChunkEndTime(0, 0));
|
||||
|
||||
//this->getLogManager() << Kernel::LogLevel_Warning << "You can stop this scenario " <<"\n";
|
||||
m_chunksVerify = -1;
|
||||
m_endProcess = true;
|
||||
}
|
||||
else
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Info << "End of data gathering...calculating b matrix" << "\n";
|
||||
|
||||
m_eegFile.close();
|
||||
m_eogFile.close();
|
||||
|
||||
m_eegFile.open(m_eegTempFilename, std::ios::in | std::ios::app);
|
||||
if (m_eegFile.fail())
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "Opening [" << m_eegTempFilename << "] for reading failed\n";
|
||||
return false;
|
||||
}
|
||||
|
||||
m_eogFile.open(m_eogTempFilename, std::ios::in | std::ios::app);
|
||||
if (m_eogFile.fail())
|
||||
{
|
||||
this->getLogManager() << Kernel::LogLevel_Error << "Opening [" << m_eogTempFilename << "] for reading failed\n";
|
||||
return false;
|
||||
}
|
||||
|
||||
//Process to extract the Matrix B
|
||||
double aux;
|
||||
|
||||
Eigen::MatrixXd data0(m_nChannels0, m_nSamples0 * m_nChunks); //EEG data
|
||||
Eigen::MatrixXd data1(m_nChannels1, m_nSamples1 * m_nChunks); //EOG data
|
||||
|
||||
for (size_t k = 0; k < m_nChunks; ++k)
|
||||
{
|
||||
for (size_t i = 0; i < m_nChannels0; ++i) //Number of channels
|
||||
{
|
||||
for (size_t j = 0; j < m_nSamples0; ++j) //Number of Samples per Chunk
|
||||
{
|
||||
m_eegFile >> aux;
|
||||
data0(i, j + k * m_nSamples0) = aux;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for (size_t k = 0; k < m_nChunks; ++k)
|
||||
{
|
||||
for (size_t i = 0; i < m_nChannels1; ++i) //Number of channels
|
||||
{
|
||||
for (size_t j = 0; j < m_nSamples1; ++j) //Number of Samples per Chunk
|
||||
{
|
||||
m_eogFile >> aux;
|
||||
data1(i, j + k * m_nSamples1) = aux;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// We will eliminate the firsts and lasts chunks of each channel
|
||||
|
||||
Eigen::MatrixXd data0N(m_nChannels0, m_nSamples0 * m_nChunks); //EEG data
|
||||
Eigen::MatrixXd data1N(m_nChannels1, m_nSamples1 * m_nChunks); //EOG data
|
||||
|
||||
size_t validChunks = m_endTimeChunks - m_startTimeChunks;
|
||||
|
||||
size_t iblockeeg = 0;
|
||||
size_t jblockeeg = m_startTimeChunks * m_nSamples0 - 1;
|
||||
size_t pblockeeg = m_nChannels0;
|
||||
size_t qblockeeg = m_nSamples0 * (validChunks);
|
||||
size_t iblockeog = 0;
|
||||
size_t jblockeog = m_startTimeChunks * m_nSamples1 - 1;
|
||||
size_t pblockeog = m_nChannels1;
|
||||
size_t qblockeog = m_nSamples1 * (validChunks);
|
||||
|
||||
|
||||
data0N = data0.block(iblockeeg, jblockeeg, pblockeeg, qblockeeg);
|
||||
data1N = data1.block(iblockeog, jblockeog, pblockeog, qblockeog);
|
||||
|
||||
|
||||
double nVal = 0;
|
||||
double min = 1e-6;
|
||||
double max = 1e6;
|
||||
|
||||
Eigen::VectorXd meanRowEEG(m_nChannels0);
|
||||
Eigen::VectorXd meanRowEog(m_nChannels1);
|
||||
|
||||
meanRowEEG.setZero(m_nChannels0, 1);
|
||||
meanRowEog.setZero(m_nChannels1, 1);
|
||||
|
||||
|
||||
for (size_t i = 0; i < m_nChannels0; ++i) //Number of channels
|
||||
{
|
||||
nVal = 0;
|
||||
for (size_t j = 0; j < m_nSamples0 * validChunks; ++j) //Number of Samples per Chunk
|
||||
{
|
||||
if ((data0N(i, j) > -max && data0N(i, j) < -min) || (data0N(i, j) > min && data0N(i, j) < max))
|
||||
{
|
||||
//Valid Interval
|
||||
meanRowEEG(i) = meanRowEEG(i) + data0N(i, j);
|
||||
nVal = nVal + 1;
|
||||
}
|
||||
}
|
||||
if (nVal != 0) { meanRowEEG(i) = meanRowEEG(i) / nVal; }
|
||||
else { meanRowEEG(i) = 0; }
|
||||
}
|
||||
|
||||
|
||||
for (size_t i = 0; i < m_nChannels1; ++i) //Number of channels
|
||||
{
|
||||
nVal = 0;
|
||||
for (size_t j = 0; j < m_nSamples1 * validChunks; ++j) //Number of Samples per Chunk
|
||||
{
|
||||
if ((data1N(i, j) > -max && data1N(i, j) < -min) || (data1N(i, j) > min && data1N(i, j) < max))
|
||||
{
|
||||
//Valid Interval
|
||||
meanRowEog(i) = meanRowEog(i) + data1N(i, j);
|
||||
nVal = nVal + 1;
|
||||
}
|
||||
}
|
||||
if (nVal != 0) { meanRowEog(i) = meanRowEog(i) / nVal; }
|
||||
else { meanRowEog(i) = 0; }
|
||||
}
|
||||
|
||||
|
||||
// The values which are not valid (very large or very small) will be set to the mean value
|
||||
// So these values will not influence to the covariance calcul because the covariance is centered (value - mean)
|
||||
|
||||
|
||||
for (size_t i = 0; i < m_nChannels0; ++i) //Number of channels
|
||||
{
|
||||
for (size_t j = 0; j < m_nSamples0 * validChunks; ++j) //Number of total samples
|
||||
{
|
||||
if ((data0N(i, j) > -max && data0N(i, j) < -min) || (data0N(i, j) > min && data0N(i, j) < max))
|
||||
{
|
||||
//Valid Interval
|
||||
data0N(i, j) = data0N(i, j) - meanRowEEG(i);
|
||||
}
|
||||
else { data0N(i, j) = 0; } //Invalid
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
for (size_t i = 0; i < m_nChannels1; ++i) //Number of channels
|
||||
{
|
||||
for (size_t j = 0; j < m_nSamples1 * validChunks; ++j) //Number of total samples
|
||||
{
|
||||
if ((data1N(i, j) > -max && data1N(i, j) < -min) || (data1N(i, j) > min && data1N(i, j) < max))
|
||||
{
|
||||
//Valid Interval
|
||||
data1N(i, j) = data1N(i, j) - meanRowEog(i);
|
||||
}
|
||||
else { data1N(i, j) = 0; } //Invalid
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
//Now we need to calculate the matrix b (which tells us the correct weights to be stored in b matrix)
|
||||
|
||||
|
||||
Eigen::MatrixXd noiseCoeff(m_nChannels0, m_nChannels1); //Noise Coefficients Matrix (Dim: Channels EEG x Channels EOG)
|
||||
Eigen::MatrixXd covEog(m_nChannels1, m_nChannels1);
|
||||
Eigen::MatrixXd covEogInv(m_nChannels1, m_nChannels1);
|
||||
Eigen::MatrixXd covEegAndEog(m_nChannels0, m_nChannels1);
|
||||
|
||||
covEog = (data1N * data1N.transpose());
|
||||
|
||||
covEogInv = covEog.inverse();
|
||||
|
||||
covEegAndEog = data0N * (data1N.transpose());
|
||||
|
||||
noiseCoeff = covEegAndEog * covEogInv;
|
||||
|
||||
|
||||
// Save Matrix b to the file specified in the parameters
|
||||
|
||||
|
||||
m_matrixFile << m_nChannels0 << " " << m_nChannels1 << " " << m_nSamples0 << "\n";
|
||||
|
||||
for (size_t i = 0; i < m_nChannels0; ++i) //Number of channels EEG
|
||||
{
|
||||
for (size_t j = 0; j < m_nChannels1; ++j) { m_matrixFile << noiseCoeff(i, j) << "\n"; } //Number of channels EOG
|
||||
}
|
||||
|
||||
|
||||
m_eegFile.close();
|
||||
m_eogFile.close();
|
||||
m_matrixFile.close();
|
||||
|
||||
m_chunksVerify = -1;
|
||||
m_endProcess = true;
|
||||
|
||||
|
||||
this->getLogManager() << Kernel::LogLevel_Info << "b Matrix was successfully calculated" << "\n";
|
||||
this->getLogManager() << Kernel::LogLevel_Info << "Wrote the matrix to [" << m_calibrationFilename << "]\n";
|
||||
|
||||
//this->getLogManager() << Kernel::LogLevel_Warning << "You can stop this scenario " <<"\n";
|
||||
|
||||
m_stimulationEncoder.getInputStimulationSet()->clear();
|
||||
m_stimulationEncoder.getInputStimulationSet()->appendStimulation(OVTK_StimulationId_TrainCompleted, 0, 0);
|
||||
m_stimulationEncoder.encodeBuffer();
|
||||
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, 0), boxContext.getInputChunkEndTime(0, 0));
|
||||
}
|
||||
}
|
||||
|
||||
m_time++;
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmEOG_Denoising_Calibration::processInput(const size_t /*index*/)
|
||||
{
|
||||
getBoxAlgorithmContext()->markAlgorithmAsReadyToProcess();
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
bool CBoxAlgorithmEOG_Denoising_Calibration::process()
|
||||
{
|
||||
Kernel::IBoxIO& boxContext = this->getDynamicBoxContext();
|
||||
|
||||
if (m_endProcess)
|
||||
{
|
||||
// We have done our stuff and have sent out a stimuli that we're done. However, if we're called again, we just do nothing,
|
||||
// but do not return false (==error) as this state is normal after training.
|
||||
return true;
|
||||
}
|
||||
|
||||
// Signal EEG
|
||||
for (size_t i = 0; i < boxContext.getInputChunkCount(0); ++i)
|
||||
{
|
||||
m_algo0SignalDecoder.decode(i);
|
||||
|
||||
m_nChannels0 = m_algo0SignalDecoder.getOutputMatrix()->getDimensionSize(0);
|
||||
m_nSamples0 = m_algo0SignalDecoder.getOutputMatrix()->getDimensionSize(1);
|
||||
|
||||
CMatrix* matrix0 = m_algo0SignalDecoder.getOutputMatrix();
|
||||
double* buffer0 = matrix0->getBuffer();
|
||||
|
||||
for (size_t j = 0; j < matrix0->getBufferElementCount(); ++j) { m_eegFile << buffer0[j] << "\n"; }
|
||||
}
|
||||
//Signal EOG
|
||||
for (size_t i = 0; i < boxContext.getInputChunkCount(1); ++i)
|
||||
{
|
||||
m_algo1SignalDecoder.decode(i);
|
||||
|
||||
m_nChannels1 = m_algo1SignalDecoder.getOutputMatrix()->getDimensionSize(0);
|
||||
m_nSamples1 = m_algo1SignalDecoder.getOutputMatrix()->getDimensionSize(1);
|
||||
|
||||
CMatrix* matrix1 = m_algo1SignalDecoder.getOutputMatrix();
|
||||
double* buffer1 = matrix1->getBuffer();
|
||||
|
||||
for (size_t j = 0; j < matrix1->getBufferElementCount(); ++j) { m_eogFile << buffer1[j] << "\n"; }
|
||||
|
||||
m_nChunks++;
|
||||
}
|
||||
|
||||
|
||||
for (size_t chunk = 0; chunk < boxContext.getInputChunkCount(2); ++chunk)
|
||||
{
|
||||
m_algo2StimulationDecoder.decode(chunk);
|
||||
for (size_t j = 0; j < m_algo2StimulationDecoder.getOutputStimulationSet()->getStimulationCount(); ++j)
|
||||
{
|
||||
if (m_algo2StimulationDecoder.getOutputStimulationSet()->getStimulationIdentifier(j) == 33025)
|
||||
{
|
||||
m_startTime = m_time;
|
||||
this->getLogManager() << Kernel::LogLevel_Info << "Start time: " << m_startTime << "\n";
|
||||
}
|
||||
|
||||
if (m_algo2StimulationDecoder.getOutputStimulationSet()->getStimulationIdentifier(j) == 33031)
|
||||
{
|
||||
m_endTime = m_time;
|
||||
this->getLogManager() << Kernel::LogLevel_Info << "End time: " << m_endTime << "\n";
|
||||
}
|
||||
|
||||
// m_trainDate = m_algo2StimulationDecoder.getOutputStimulationSet()->getStimulationDate(m_algo2StimulationDecoder.getOutputStimulationSet()->getStimulationCount());
|
||||
m_trainDate = m_algo2StimulationDecoder.getOutputStimulationSet()->getStimulationDate(j);
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
#endif
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+142
@@ -0,0 +1,142 @@
|
||||
#pragma once
|
||||
|
||||
#if defined(TARGET_HAS_ThirdPartyEIGEN)
|
||||
|
||||
//You may have to change this path to match your folder organisation
|
||||
#include "../ovp_defines.h"
|
||||
|
||||
#include <openvibe/ov_all.h>
|
||||
#include <toolkit/ovtk_all.h>
|
||||
|
||||
#include <iostream>
|
||||
#include <fstream>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
/**
|
||||
* \class CBoxAlgorithmEOG_Denoising_Calibration
|
||||
* \author Joao-Pedro Berti-Ligabo / Inria
|
||||
* \date Fri May 23 15:30:58 2014
|
||||
* \brief The class CBoxAlgorithmEOG_Denoising_Calibration describes the box EOG_Denoising_Calibration.
|
||||
*
|
||||
*/
|
||||
class CBoxAlgorithmEOG_Denoising_Calibration final : virtual public Toolkit::TBoxAlgorithm<IBoxAlgorithm>
|
||||
{
|
||||
public:
|
||||
void release() override { delete this; }
|
||||
|
||||
bool initialize() override;
|
||||
bool uninitialize() override;
|
||||
|
||||
bool processClock(Kernel::CMessageClock& msg) override;
|
||||
bool processInput(const size_t index) override;
|
||||
|
||||
// If you want to use processClock, you must provide the clock frequency.
|
||||
uint64_t getClockFrequency() override { return 1LL << 32; } // the box clock frequency
|
||||
|
||||
bool process() override;
|
||||
|
||||
//virtual bool openfile();
|
||||
|
||||
// As we do with any class in openvibe, we use the macro below
|
||||
// to associate this box to an unique identifier.
|
||||
// The inheritance information is also made available,
|
||||
// as we provide the superclass Toolkit::TBoxAlgorithm < IBoxAlgorithm >
|
||||
_IsDerivedFromClass_Final_(Toolkit::TBoxAlgorithm<IBoxAlgorithm>, OVP_ClassId_BoxAlgorithm_EOG_Denoising_Calibration)
|
||||
|
||||
protected:
|
||||
Toolkit::TSignalDecoder<CBoxAlgorithmEOG_Denoising_Calibration> m_algo0SignalDecoder;
|
||||
Toolkit::TSignalDecoder<CBoxAlgorithmEOG_Denoising_Calibration> m_algo1SignalDecoder;
|
||||
|
||||
Toolkit::TStimulationDecoder<CBoxAlgorithmEOG_Denoising_Calibration> m_algo2StimulationDecoder;
|
||||
Toolkit::TStimulationEncoder<CBoxAlgorithmEOG_Denoising_Calibration> m_stimulationEncoder;
|
||||
|
||||
CString m_calibrationFilename;
|
||||
|
||||
size_t m_chunksVerify = 0;
|
||||
size_t m_nChunks = 0;
|
||||
bool m_endProcess = false;
|
||||
std::fstream m_eegFile;
|
||||
std::fstream m_eogFile;
|
||||
std::ofstream m_matrixFile;
|
||||
|
||||
double m_startTime = 0;
|
||||
double m_endTime = 0;
|
||||
|
||||
size_t m_startTimeChunks = 0;
|
||||
size_t m_endTimeChunks = 0;
|
||||
|
||||
uint64_t m_trainDate = 0;
|
||||
uint64_t m_trainChunkStartTime = 0;
|
||||
uint64_t m_trainChunkEndTime = 0;
|
||||
|
||||
double m_time = 0;
|
||||
|
||||
size_t m_nChannels0 = 0;
|
||||
size_t m_nChannels1 = 0;
|
||||
|
||||
size_t m_nSamples0 = 0;
|
||||
size_t m_nSamples1 = 0;
|
||||
|
||||
uint64_t m_stimID = 0;
|
||||
|
||||
CString m_eegTempFilename;
|
||||
CString m_eogTempFilename;
|
||||
};
|
||||
|
||||
/**
|
||||
* \class CBoxAlgorithmEOG_Denoising_CalibrationDesc
|
||||
* \author Joao-Pedro Berti-Ligabo / Inria
|
||||
* \date Fri May 23 15:30:58 2014
|
||||
* \brief Descriptor of the box EOG_Denoising_Calibration.
|
||||
*
|
||||
*/
|
||||
class CBoxAlgorithmEOG_Denoising_CalibrationDesc final : virtual public IBoxAlgorithmDesc
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { }
|
||||
|
||||
CString getName() const override { return CString("EOG Denoising Calibration"); }
|
||||
CString getAuthorName() const override { return CString("Joao-Pedro Berti-Ligabo"); }
|
||||
CString getAuthorCompanyName() const override { return CString("Inria"); }
|
||||
|
||||
CString getShortDescription() const override { return CString("Algorithm implementation as suggested in Schlogl's article of 2007."); }
|
||||
|
||||
CString getDetailedDescription() const override
|
||||
{
|
||||
return CString("Press 'a' to set start point and 'u' to set end point, you can connect the Keyboard Stimulator for that");
|
||||
}
|
||||
|
||||
CString getCategory() const override { return CString("Signal processing/Denoising"); }
|
||||
CString getVersion() const override { return CString("1.0"); }
|
||||
CString getStockItemName() const override { return CString("gnome-fs-regular.png"); }
|
||||
|
||||
CIdentifier getCreatedClass() const override { return OVP_ClassId_BoxAlgorithm_EOG_Denoising_Calibration; }
|
||||
IPluginObject* create() override { return new CBoxAlgorithmEOG_Denoising_Calibration; }
|
||||
|
||||
bool getBoxPrototype(Kernel::IBoxProto& prototype) const override
|
||||
{
|
||||
prototype.addInput("EEG",OV_TypeId_Signal);
|
||||
prototype.addInput("EOG",OV_TypeId_Signal);
|
||||
prototype.addInput("Stimulations", OV_TypeId_Stimulations);
|
||||
|
||||
prototype.addSetting("Filename b Matrix", OV_TypeId_Filename, "b-Matrix-EEG.cfg");
|
||||
prototype.addSetting("End trigger", OV_TypeId_Stimulation, "OVTK_GDF_End_Of_Session");
|
||||
|
||||
prototype.addOutput("Train-completed Flag",OV_TypeId_Stimulations);
|
||||
|
||||
prototype.addFlag(OV_AttributeId_Box_FlagIsUnstable);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(IBoxAlgorithmDesc, OVP_ClassId_BoxAlgorithm_EOG_Denoising_CalibrationDesc)
|
||||
};
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
|
||||
|
||||
#endif
|
||||
+189
@@ -0,0 +1,189 @@
|
||||
#if defined(TARGET_HAS_ThirdPartyFFTW3) // required by wavelet2s
|
||||
|
||||
#include "ovpCBoxAlgorithmInverse_DWT.h"
|
||||
|
||||
#include <vector>
|
||||
#include <map>
|
||||
#include <math.h>
|
||||
#include <iostream>
|
||||
#include <string>
|
||||
|
||||
#include "../../../contrib/packages/wavelet2d/wavelet2s.h"
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
bool CBoxAlgorithmInverse_DWT::initialize()
|
||||
{
|
||||
const size_t nInput = this->getStaticBoxContext().getInputCount();
|
||||
m_algoInfoDecoder.initialize(*this, 0);
|
||||
|
||||
m_waveletType = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 0);
|
||||
m_decompositionLevel = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 1);
|
||||
|
||||
m_algoXDecoder = new Toolkit::TSignalDecoder<CBoxAlgorithmInverse_DWT> [nInput];
|
||||
|
||||
for (size_t o = 0; o < nInput - 1; ++o) { m_algoXDecoder[o].initialize(*this, o + 1); }
|
||||
|
||||
m_encoder.initialize(*this, 0);
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmInverse_DWT::uninitialize()
|
||||
{
|
||||
const size_t nInput = this->getStaticBoxContext().getInputCount();
|
||||
|
||||
m_algoInfoDecoder.uninitialize();
|
||||
|
||||
for (size_t o = 0; o < nInput - 1; ++o) { m_algoXDecoder[o].uninitialize(); }
|
||||
|
||||
if (m_algoXDecoder)
|
||||
{
|
||||
delete[] m_algoXDecoder;
|
||||
m_algoXDecoder = nullptr;
|
||||
}
|
||||
|
||||
m_encoder.uninitialize();
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
bool CBoxAlgorithmInverse_DWT::processInput(const size_t /*index*/)
|
||||
{
|
||||
getBoxAlgorithmContext()->markAlgorithmAsReadyToProcess();
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmInverse_DWT::process()
|
||||
{
|
||||
// the dynamic box context describes the current state of the box inputs and outputs (i.e. the chunks)
|
||||
Kernel::IBoxIO& boxContext = this->getDynamicBoxContext();
|
||||
|
||||
// size_t J = std::atoi(m_decompositionLevel);
|
||||
const size_t nInput = this->getStaticBoxContext().getInputCount();
|
||||
const std::string nm(m_waveletType.toASCIIString());
|
||||
std::vector<std::vector<double>> dwtop;
|
||||
std::vector<std::vector<double>> idwt_output;
|
||||
std::vector<std::vector<double>> flag;
|
||||
std::vector<std::vector<size_t>> length;
|
||||
|
||||
size_t flagReceveid = 0;
|
||||
|
||||
std::vector<size_t> nChannels(nInput);
|
||||
std::vector<size_t> nSamples(nInput);
|
||||
|
||||
//Check if all inputs have some information to decode
|
||||
if (boxContext.getInputChunkCount(nInput - 1) == 1)
|
||||
{
|
||||
//Decode the first input (Informations)
|
||||
for (size_t i = 0; i < boxContext.getInputChunkCount(0); ++i)
|
||||
{
|
||||
m_algoInfoDecoder.decode(i);
|
||||
|
||||
nChannels[0] = m_algoInfoDecoder.getOutputMatrix()->getDimensionSize(0);
|
||||
nSamples[0] = m_algoInfoDecoder.getOutputMatrix()->getDimensionSize(1);
|
||||
|
||||
|
||||
CMatrix* matrix = m_algoInfoDecoder.getOutputMatrix();
|
||||
double* buffer = matrix->getBuffer();
|
||||
|
||||
//this->getLogManager() << Kernel::LogLevel_Warning << "buffer 0 " << (size_t)buffer0[0] << "\n";
|
||||
|
||||
length.resize(nChannels[0]);
|
||||
flag.resize(nChannels[0]);
|
||||
|
||||
for (size_t j = 0; j < nChannels[0]; ++j)
|
||||
{
|
||||
size_t f = 0;
|
||||
for (size_t l = 0; l < size_t(buffer[0]); ++l)
|
||||
{
|
||||
length[j].push_back(size_t(buffer[l + 1]));
|
||||
f = l;
|
||||
}
|
||||
|
||||
for (size_t l = 0; l < size_t(buffer[f + 2]); ++l) { flag[j].push_back(size_t(buffer[f + 3 + l])); }
|
||||
}
|
||||
flagReceveid = 1;
|
||||
}
|
||||
|
||||
//If Informations is decoded
|
||||
if (flagReceveid == 1)
|
||||
{
|
||||
//Decode each decomposition level
|
||||
for (size_t o = 0; o < nInput - 1; ++o)
|
||||
{
|
||||
//Decode data of channels
|
||||
for (size_t i = 0; i < boxContext.getInputChunkCount(o + 1); ++i)
|
||||
{
|
||||
m_algoXDecoder[o].decode(i);
|
||||
nChannels[o + 1] = m_algoXDecoder[o].getOutputMatrix()->getDimensionSize(0);
|
||||
nSamples[o + 1] = m_algoXDecoder[o].getOutputMatrix()->getDimensionSize(1);
|
||||
CMatrix* matrix = m_algoXDecoder[o].getOutputMatrix();
|
||||
double* buffer = matrix->getBuffer();
|
||||
|
||||
//dwtop is the dwt coefficients
|
||||
dwtop.resize(nChannels[0]);
|
||||
|
||||
//Store input data (dwt coefficients) in just one vector (dwtop)
|
||||
for (size_t j = 0; j < nChannels[0]; ++j)
|
||||
{
|
||||
for (size_t k = 0; k < nSamples[o + 1]; ++k) { dwtop[j].push_back(buffer[k + j * nSamples[o + 1]]); }
|
||||
}
|
||||
|
||||
//Check if received informations about dwt box are coherent with inverse dwt box settings
|
||||
if (!length[0].empty() && o == nInput - 2)
|
||||
{
|
||||
//Check if quantity of samples received are the same
|
||||
if (length[0].at(nInput - 1) == dwtop[0].size())
|
||||
{
|
||||
//Resize idwt vector
|
||||
idwt_output.resize(nChannels[0]);
|
||||
|
||||
//Calculate idwt for each channel
|
||||
for (size_t j = 0; j < nChannels[0]; ++j) { idwt(dwtop[j], flag[j], nm, idwt_output[j], length[j]); }
|
||||
|
||||
|
||||
m_encoder.getInputSamplingRate() = 2 * m_algoXDecoder[o].getOutputSamplingRate();
|
||||
|
||||
m_encoder.getInputMatrix()->resize(nChannels[0], length[0].at(nInput - 1));
|
||||
|
||||
|
||||
for (size_t j = 0; j < nChannels[0]; j++)
|
||||
{
|
||||
m_encoder.getInputMatrix()->setDimensionLabel(0, j, m_algoXDecoder[o].getOutputMatrix()->getDimensionLabel(0, j));
|
||||
}
|
||||
|
||||
|
||||
//Encode resultant signal to output
|
||||
for (size_t j = 0; j < nChannels[0]; ++j)
|
||||
{
|
||||
for (size_t k = 0; k < size_t(idwt_output[j].size()); ++k)
|
||||
{
|
||||
m_encoder.getInputMatrix()->getBuffer()[k + j * size_t(idwt_output[j].size())] = idwt_output[j][k];
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
m_encoder.encodeHeader();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
m_encoder.encodeBuffer();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
m_encoder.encodeEnd();
|
||||
boxContext.markOutputAsReadyToSend(0, boxContext.getInputChunkStartTime(0, i), boxContext.getInputChunkEndTime(0, i));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
#endif
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+140
@@ -0,0 +1,140 @@
|
||||
#pragma once
|
||||
|
||||
#if defined(TARGET_HAS_ThirdPartyFFTW3) // required by wavelet2s
|
||||
|
||||
//You may have to change this path to match your folder organisation
|
||||
#include "../ovp_defines.h"
|
||||
|
||||
#include <openvibe/ov_all.h>
|
||||
#include <toolkit/ovtk_all.h>
|
||||
#include <string>
|
||||
#include <sstream>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
/**
|
||||
* \class CBoxAlgorithmInverse_DWT
|
||||
* \author Joao-Pedro Berti-Ligabo / Inria
|
||||
* \date Thu Jul 24 10:57:05 2014
|
||||
* \brief The class CBoxAlgorithmInverse_DWT describes the box Inverse DWT.
|
||||
*
|
||||
*/
|
||||
class CBoxAlgorithmInverse_DWT final : virtual public Toolkit::TBoxAlgorithm<IBoxAlgorithm>
|
||||
{
|
||||
public:
|
||||
CBoxAlgorithmInverse_DWT() { }
|
||||
|
||||
void release() override { delete this; }
|
||||
|
||||
bool initialize() override;
|
||||
bool uninitialize() override;
|
||||
|
||||
bool processInput(const size_t index) override;
|
||||
bool process() override;
|
||||
|
||||
|
||||
_IsDerivedFromClass_Final_(Toolkit::TBoxAlgorithm<IBoxAlgorithm>, OVP_ClassId_BoxAlgorithm_Inverse_DWT)
|
||||
|
||||
protected:
|
||||
// Codec algorithms specified in the skeleton-generator:
|
||||
// Signal stream encoder
|
||||
Toolkit::TSignalEncoder<CBoxAlgorithmInverse_DWT> m_encoder;
|
||||
Toolkit::TSignalDecoder<CBoxAlgorithmInverse_DWT> m_algoInfoDecoder;
|
||||
Toolkit::TSignalDecoder<CBoxAlgorithmInverse_DWT>* m_algoXDecoder = nullptr;
|
||||
|
||||
CString m_waveletType;
|
||||
CString m_decompositionLevel;
|
||||
};
|
||||
|
||||
|
||||
class CBoxAlgorithmInverse_DWTListener final : public Toolkit::TBoxListener<IBoxListener>
|
||||
{
|
||||
public:
|
||||
|
||||
bool onSettingValueChanged(Kernel::IBox& box, const size_t index) override
|
||||
{
|
||||
if (index == 0) { return true; }
|
||||
|
||||
if (index == 1)
|
||||
{
|
||||
const size_t nInput = box.getInputCount();
|
||||
|
||||
CString nDecompositionLevels;
|
||||
box.getSettingValue(1, nDecompositionLevels);
|
||||
|
||||
const size_t nDecompositionLevel = atoi(nDecompositionLevels);
|
||||
|
||||
if (nInput != nDecompositionLevel + 2)
|
||||
{
|
||||
for (size_t i = 0; i < nInput; ++i) { box.removeInput(nInput - i - 1); }
|
||||
|
||||
box.addInput("Info",OV_TypeId_Signal);
|
||||
box.addInput("A",OV_TypeId_Signal);
|
||||
for (size_t i = nDecompositionLevel; i > 0; i--) { box.addInput(("D" + std::to_string(i)).c_str(),OV_TypeId_Signal); }
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(Toolkit::TBoxListener<IBoxListener>, CIdentifier::undefined())
|
||||
};
|
||||
|
||||
|
||||
/**
|
||||
* \class CBoxAlgorithmInverse_DWTDesc
|
||||
* \author Joao-Pedro Berti-Ligabo / Inria
|
||||
* \date Thu Jul 24 10:57:05 2014
|
||||
* \brief Descriptor of the box Inverse DWT.
|
||||
*
|
||||
*/
|
||||
class CBoxAlgorithmInverse_DWTDesc final : virtual public IBoxAlgorithmDesc
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { }
|
||||
|
||||
CString getName() const override { return CString("Inverse DWT"); }
|
||||
CString getAuthorName() const override { return CString("Joao-Pedro Berti-Ligabo"); }
|
||||
CString getAuthorCompanyName() const override { return CString("Inria"); }
|
||||
CString getShortDescription() const override { return CString("Calculate Inverse DiscreteWaveletTransform"); }
|
||||
|
||||
CString getDetailedDescription() const override { return CString("Calculate Inverse DiscreteWaveletTransform using different types of wavelets"); }
|
||||
|
||||
CString getCategory() const override { return CString("Signal processing/Wavelets"); }
|
||||
CString getVersion() const override { return CString("1.0"); }
|
||||
CString getStockItemName() const override { return CString("gnome-fs-regular.png"); }
|
||||
|
||||
CIdentifier getCreatedClass() const override { return OVP_ClassId_BoxAlgorithm_Inverse_DWT; }
|
||||
IPluginObject* create() override { return new CBoxAlgorithmInverse_DWT; }
|
||||
|
||||
|
||||
IBoxListener* createBoxListener() const override { return new CBoxAlgorithmInverse_DWTListener; }
|
||||
void releaseBoxListener(IBoxListener* listener) const override { delete listener; }
|
||||
|
||||
bool getBoxPrototype(Kernel::IBoxProto& prototype) const override
|
||||
{
|
||||
prototype.addInput("Info",OV_TypeId_Signal);
|
||||
prototype.addInput("A",OV_TypeId_Signal);
|
||||
prototype.addInput("D2",OV_TypeId_Signal);
|
||||
prototype.addInput("D1",OV_TypeId_Signal);
|
||||
|
||||
prototype.addOutput("Signal",OV_TypeId_Signal);
|
||||
|
||||
prototype.addSetting("Wavelet type",OVP_TypeId_WaveletType, "");
|
||||
prototype.addSetting("Wavelet decomposition levels",OVP_TypeId_WaveletLevel, "");
|
||||
|
||||
prototype.addFlag(OV_AttributeId_Box_FlagIsUnstable);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(IBoxAlgorithmDesc, OVP_ClassId_BoxAlgorithm_Inverse_DWTDesc)
|
||||
};
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
|
||||
|
||||
#endif
|
||||
+178
@@ -0,0 +1,178 @@
|
||||
//include OpenViBE
|
||||
#include "ovpCBoxAlgorithmQuadraticForm.h"
|
||||
|
||||
//include C++ STL
|
||||
#include <iostream>
|
||||
#include <sstream>
|
||||
//atoi
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
bool CBoxAlgorithmQuadraticForm::initialize()
|
||||
{
|
||||
//the algorithms that decode and encode the signals
|
||||
m_decoder = &getAlgorithmManager().getAlgorithm(getAlgorithmManager().createAlgorithm(OVP_GD_ClassId_Algorithm_SignalDecoder));
|
||||
m_encoder = &getAlgorithmManager().getAlgorithm(getAlgorithmManager().createAlgorithm(OVP_GD_ClassId_Algorithm_SignalEncoder));
|
||||
m_decoder->initialize();
|
||||
m_encoder->initialize();
|
||||
m_decoder->getOutputParameter(OVP_GD_Algorithm_SignalDecoder_OutputParameterId_Sampling)->setReferenceTarget(
|
||||
m_encoder->getInputParameter(OVP_GD_Algorithm_SignalEncoder_InputParameterId_Sampling));
|
||||
|
||||
//connecting the decoding and encoding the parameters
|
||||
m_iEBMLBufferHandle.initialize(m_decoder->getInputParameter(OVP_GD_Algorithm_SignalDecoder_InputParameterId_MemoryBufferToDecode));
|
||||
m_iMatrixHandle.initialize(m_decoder->getOutputParameter(OVP_GD_Algorithm_StreamedMatrixDecoder_OutputParameterId_Matrix));
|
||||
m_oMatrixHandle.initialize(m_encoder->getInputParameter(OVP_GD_Algorithm_StreamedMatrixEncoder_InputParameterId_Matrix));
|
||||
m_oEBMLBufferHandle.initialize(m_encoder->getOutputParameter(OVP_GD_Algorithm_SignalEncoder_OutputParameterId_EncodedMemoryBuffer));
|
||||
|
||||
//end and start time
|
||||
m_startTime = 0;
|
||||
m_endTime = 0;
|
||||
|
||||
//reading the quadratic operator (matrix) values
|
||||
|
||||
//the number of rows/columns
|
||||
const size_t nRow = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 1);
|
||||
|
||||
//setting the size of the matrix
|
||||
m_quadraticOperator.resize(nRow, nRow);
|
||||
|
||||
//the coefficients
|
||||
const CString coefs = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 0);
|
||||
const char* str = coefs.toASCIIString();
|
||||
double* buffer = m_quadraticOperator.getBuffer();
|
||||
|
||||
std::istringstream iss(str); //the stream for parsing the matrix coefficient
|
||||
double currentValue = 0.0; //the current coefficient being read
|
||||
|
||||
for (size_t i = 0; i < nRow; ++i)
|
||||
{
|
||||
for (size_t j = 0; j < nRow; ++j)
|
||||
{
|
||||
//actual parsing, checking and storing value if everything is OK
|
||||
if (!(iss >> currentValue))
|
||||
{
|
||||
getBoxAlgorithmContext()->getPlayerContext()->getLogManager() << Kernel::LogLevel_Error <<
|
||||
"Error reading quadratic operator coefficients\n The coefficients or the number of coefficient must be wrong\n";
|
||||
return false;
|
||||
}
|
||||
buffer[i * nRow + j] = currentValue;
|
||||
}
|
||||
}
|
||||
|
||||
if (iss >> currentValue)
|
||||
{
|
||||
getBoxAlgorithmContext()->getPlayerContext()->getLogManager() << Kernel::LogLevel_Warning <<
|
||||
"There may be more coefficients specified in the setting 'Matrix values' than the number of rows/columns can allow\n";
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmQuadraticForm::uninitialize()
|
||||
{
|
||||
//uninitializing algorithms and parameters handlers
|
||||
m_decoder->uninitialize();
|
||||
m_encoder->uninitialize();
|
||||
m_iEBMLBufferHandle.uninitialize();
|
||||
m_oEBMLBufferHandle.uninitialize();
|
||||
m_iMatrixHandle.uninitialize();
|
||||
m_oMatrixHandle.uninitialize();
|
||||
|
||||
//releasing algorithms
|
||||
getAlgorithmManager().releaseAlgorithm(*m_decoder);
|
||||
getAlgorithmManager().releaseAlgorithm(*m_encoder);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmQuadraticForm::processInput(const size_t /*index*/)
|
||||
{
|
||||
//if input is arrived, processing it, i.e., computing the corresponding quadratic forms
|
||||
getBoxAlgorithmContext()->markAlgorithmAsReadyToProcess();
|
||||
return true;
|
||||
}
|
||||
|
||||
bool CBoxAlgorithmQuadraticForm::process()
|
||||
{
|
||||
Kernel::IBoxIO& boxContext = this->getDynamicBoxContext();
|
||||
|
||||
//prcessing the input buffers
|
||||
for (size_t i = 0; i < boxContext.getInputChunkCount(0); ++i)
|
||||
{
|
||||
//decoding the input signal
|
||||
m_iEBMLBufferHandle = boxContext.getInputChunk(0, i);
|
||||
m_decoder->process();
|
||||
//storing the start and end time of the chunk
|
||||
m_startTime = boxContext.getInputChunkStartTime(0, i);
|
||||
m_endTime = boxContext.getInputChunkEndTime(0, i);
|
||||
|
||||
//deal with the header if needed (initializations)
|
||||
if (m_decoder->isOutputTriggerActive(OVP_GD_Algorithm_SignalDecoder_OutputTriggerId_ReceivedHeader))
|
||||
{
|
||||
//getting some input matrix properties
|
||||
m_nChannels = m_iMatrixHandle->getDimensionSize(0);
|
||||
m_nSamplesPerBuffer = m_iMatrixHandle->getDimensionSize(1);
|
||||
|
||||
//checking that the number of channels is compatible with the quadratic operator size
|
||||
if (m_nChannels != m_quadraticOperator.getDimensionSize(0))
|
||||
{
|
||||
getBoxAlgorithmContext()->getPlayerContext()->getLogManager() << Kernel::LogLevel_Error <<
|
||||
"The number of input channels is not compatible with the number of rows/columns of the quadratic operator matrix. This number of rows/columns must be equal to the number of input channels\n";
|
||||
return false;
|
||||
}
|
||||
|
||||
//setting the size of the output buffer
|
||||
m_oMatrixHandle->resize(1, m_nSamplesPerBuffer);
|
||||
|
||||
m_oEBMLBufferHandle = boxContext.getOutputChunk(0);
|
||||
//encoding the output
|
||||
m_encoder->process(OVP_GD_Algorithm_SignalEncoder_InputTriggerId_EncodeHeader);
|
||||
//sending the output
|
||||
boxContext.markOutputAsReadyToSend(0, m_startTime, m_endTime);
|
||||
}
|
||||
|
||||
//applying the quadratic operator
|
||||
if (m_decoder->isOutputTriggerActive(OVP_GD_Algorithm_SignalDecoder_OutputTriggerId_ReceivedBuffer))
|
||||
{
|
||||
double* buffer = m_quadraticOperator.getBuffer();
|
||||
double* iBuffer = m_iMatrixHandle->getBuffer();
|
||||
double* oBuffer = m_oMatrixHandle->getBuffer();
|
||||
|
||||
//applying the quadratic operator for each sample: o = m^T * A * m
|
||||
for (size_t j = 0; j < m_nSamplesPerBuffer; ++j)
|
||||
{
|
||||
std::vector<double> prime(m_nChannels); //performing m' = A * m (intermediate step 1)
|
||||
for (size_t k = 0; k < prime.size(); ++k) { prime[k] = 0.0; } //initializing with zeros
|
||||
|
||||
//browsing the quadratic operator matrix (A) rows
|
||||
for (size_t k = 0; k < m_quadraticOperator.getDimensionSize(0); ++k)
|
||||
{
|
||||
//browsing the quadratic operator matrix (A) columns
|
||||
for (size_t l = 0; l < m_quadraticOperator.getDimensionSize(1); ++l)
|
||||
{
|
||||
prime[k] += buffer[k * m_quadraticOperator.getDimensionSize(0) + l] * iBuffer[l * m_nChannels + j];
|
||||
}
|
||||
}
|
||||
|
||||
//performing o = m^T * m' (intermediate step 2)
|
||||
double output = 0.0;
|
||||
for (size_t k = 0; k < prime.size(); ++k) { output += iBuffer[k * m_nChannels + j] * prime[k]; }
|
||||
|
||||
oBuffer[j] = output;
|
||||
}
|
||||
|
||||
boxContext.markInputAsDeprecated(0, i);
|
||||
m_oEBMLBufferHandle = boxContext.getOutputChunk(0);
|
||||
m_encoder->process(OVP_GD_Algorithm_SignalEncoder_InputTriggerId_EncodeBuffer);
|
||||
boxContext.markOutputAsReadyToSend(0, m_startTime, m_endTime);
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+90
@@ -0,0 +1,90 @@
|
||||
#pragma once
|
||||
|
||||
#include "../ovp_defines.h"
|
||||
#include <openvibe/ov_all.h>
|
||||
#include <toolkit/ovtk_all.h>
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
class CBoxAlgorithmQuadraticForm final : virtual public Toolkit::TBoxAlgorithm<IBoxAlgorithm>
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { delete this; }
|
||||
|
||||
uint64_t getClockFrequency() override { return 0; } // the box clock frequency
|
||||
bool initialize() override;
|
||||
bool uninitialize() override;
|
||||
bool processClock(Kernel::CMessageClock& /*msg*/) override { return true; }
|
||||
bool processInput(const size_t index) override;
|
||||
bool process() override;
|
||||
|
||||
_IsDerivedFromClass_Final_(Toolkit::TBoxAlgorithm<IBoxAlgorithm>, OVP_ClassId_BoxAlgorithm_QuadraticForm)
|
||||
|
||||
protected:
|
||||
|
||||
//algorithms for encoding and decoding EBML stream
|
||||
Kernel::IAlgorithmProxy* m_encoder = nullptr;
|
||||
Kernel::IAlgorithmProxy* m_decoder = nullptr;
|
||||
|
||||
//input and output buffers
|
||||
Kernel::TParameterHandler<const IMemoryBuffer*> m_iEBMLBufferHandle;
|
||||
Kernel::TParameterHandler<IMemoryBuffer*> m_oEBMLBufferHandle;
|
||||
|
||||
//the signal matrices (input and output)
|
||||
Kernel::TParameterHandler<CMatrix*> m_iMatrixHandle;
|
||||
Kernel::TParameterHandler<CMatrix*> m_oMatrixHandle;
|
||||
|
||||
//start and end times
|
||||
uint64_t m_startTime = 0;
|
||||
uint64_t m_endTime = 0;
|
||||
|
||||
//The matrix used in the quadratic form: the quadratic operator
|
||||
CMatrix m_quadraticOperator;
|
||||
|
||||
//dimensions (number of input channels and number of samples) of the input buffer
|
||||
size_t m_nChannels = 0;
|
||||
size_t m_nSamplesPerBuffer = 0;
|
||||
};
|
||||
|
||||
class CBoxAlgorithmQuadraticFormDesc final : virtual public IBoxAlgorithmDesc
|
||||
{
|
||||
public:
|
||||
|
||||
void release() override { }
|
||||
|
||||
CString getName() const override { return CString("Quadratic Form"); }
|
||||
CString getAuthorName() const override { return CString("Fabien Lotte"); }
|
||||
CString getAuthorCompanyName() const override { return CString("IRISA-INSA Rennes"); }
|
||||
|
||||
CString getShortDescription() const override { return CString("Perform a quadratic matrix operation on the input signals m (result = m^T * A * m)"); }
|
||||
|
||||
CString getDetailedDescription() const override
|
||||
{
|
||||
return CString(
|
||||
"a square matrix A (which can be seen as a spatial filter) is applied to the input signals m (a vector). Then the transpose m^T of the input signals is multiplied to the resulting vector. In other words the output o is such as: o = m^T * A * m.");
|
||||
}
|
||||
|
||||
CString getCategory() const override { return CString("Signal processing/Basic"); }
|
||||
CString getVersion() const override { return CString("0.1"); }
|
||||
CString getStockItemName() const override { return CString("gtk-missing-image"); }
|
||||
|
||||
CIdentifier getCreatedClass() const override { return OVP_ClassId_BoxAlgorithm_QuadraticForm; }
|
||||
IPluginObject* create() override { return new CBoxAlgorithmQuadraticForm; }
|
||||
|
||||
bool getBoxPrototype(Kernel::IBoxProto& prototype) const override
|
||||
{
|
||||
prototype.addInput("input signal", OV_TypeId_Signal);
|
||||
prototype.addOutput("output signal", OV_TypeId_Signal);
|
||||
prototype.addSetting("Matrix values", OV_TypeId_String, "1 0 0 1");
|
||||
prototype.addSetting("Number of rows/columns (square matrix)", OV_TypeId_Integer, "2");
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
_IsDerivedFromClass_Final_(IBoxAlgorithmDesc, OVP_ClassId_BoxAlgorithm_QuadraticFormDesc)
|
||||
};
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
Executable
+158
@@ -0,0 +1,158 @@
|
||||
#pragma once
|
||||
|
||||
// Boxes
|
||||
//---------------------------------------------------------------------------------------------------
|
||||
#define OVP_ClassId_Identity OpenViBE::CIdentifier(0x5DFFE431, 0x35215C50)
|
||||
#define OVP_ClassId_IdentityDesc OpenViBE::CIdentifier(0x54743810, 0x6A1A88CC)
|
||||
#define OVP_ClassId_TimeBasedEpoching OpenViBE::CIdentifier(0x00777FA0, 0x5DC3F560)
|
||||
#define OVP_ClassId_TimeBasedEpochingDesc OpenViBE::CIdentifier(0x00ABDABE, 0x41381683)
|
||||
#define OVP_ClassId_BoxAlgorithm_EOG_Denoising OpenViBE::CIdentifier(0xC223FF12, 0x069A987E)
|
||||
#define OVP_ClassId_BoxAlgorithm_EOG_DenoisingDesc OpenViBE::CIdentifier(0x4F9BE623, 0xF2027046)
|
||||
#define OVP_ClassId_BoxAlgorithm_EOG_Denoising_Calibration OpenViBE::CIdentifier(0xE8DFE002, 0x70389932)
|
||||
#define OVP_ClassId_BoxAlgorithm_EOG_Denoising_CalibrationDesc OpenViBE::CIdentifier(0xF4D74831, 0x88B80DCF)
|
||||
#define OVP_ClassId_BoxAlgorithm_Inverse_DWT OpenViBE::CIdentifier(0x5B5B8468, 0x212CF963)
|
||||
#define OVP_ClassId_BoxAlgorithm_Inverse_DWTDesc OpenViBE::CIdentifier(0x01B9BC9A, 0x34766AE9)
|
||||
#define OVP_ClassId_BoxAlgorithm_DiscreteWaveletTransform OpenViBE::CIdentifier(0x824194C5, 0x46D7FDE9)
|
||||
#define OVP_ClassId_BoxAlgorithm_DiscreteWaveletTransformDesc OpenViBE::CIdentifier(0x6744711B, 0xF21B59EC)
|
||||
#define OVP_ClassId_BoxAlgorithm_EpochAverage OpenViBE::CIdentifier(0x21283D9F, 0xE76FF640)
|
||||
#define OVP_ClassId_BoxAlgorithm_EpochAverageDesc OpenViBE::CIdentifier(0x95F5F43E, 0xBE629D82)
|
||||
#define OVP_ClassId_Algorithm_MatrixAverage OpenViBE::CIdentifier(0x5E5A6C1C, 0x6F6BEB03)
|
||||
#define OVP_ClassId_Algorithm_MatrixAverageDesc OpenViBE::CIdentifier(0x1992881F, 0xC938C0F2)
|
||||
#define OVP_ClassId_BoxAlgorithm_Crop OpenViBE::CIdentifier(0x7F1A3002, 0x358117BA)
|
||||
#define OVP_ClassId_BoxAlgorithm_CropDesc OpenViBE::CIdentifier(0x64D619D7, 0x26CC42C9)
|
||||
#define OVP_ClassId_BoxAlgorithm_DifferentialIntegral OpenViBE::CIdentifier(0xCE490CBF, 0xDF7BA2E2)
|
||||
#define OVP_ClassId_BoxAlgorithm_DifferentialIntegralDesc OpenViBE::CIdentifier(0xCE490CBF, 0xDF7BA2E3)
|
||||
#define OVP_ClassId_BoxAlgorithm_MatrixTranspose OpenViBE::CIdentifier(0x5E0F04B5, 0x5B5005CF)
|
||||
#define OVP_ClassId_BoxAlgorithm_MatrixTransposeDesc OpenViBE::CIdentifier(0x119249F7, 0x556C7E0D)
|
||||
#define OVP_ClassId_BoxAlgorithm_ERSPAverage OpenViBE::CIdentifier(0x3CDB4B72, 0x295D51F7)
|
||||
#define OVP_ClassId_BoxAlgorithm_ERSPAverageDesc OpenViBE::CIdentifier(0x32C45B7E, 0x2F1B7D58)
|
||||
#define OVP_ClassId_BoxAlgorithm_ARCoefficients OpenViBE::CIdentifier(0xBAADC2F3, 0xB556A07B)
|
||||
#define OVP_ClassId_BoxAlgorithm_ARCoefficientsDesc OpenViBE::CIdentifier(0xBAADC2F3, 0xB556A07A)
|
||||
#define OVP_ClassId_BoxAlgorithm_ConnectivityMeasure OpenViBE::CIdentifier(0x994a9a45, 0x4181a048)
|
||||
#define OVP_ClassId_BoxAlgorithm_ConnectivityMeasureDesc OpenViBE::CIdentifier(0xaf5e56f9, 0xcbc54c18)
|
||||
#define OVP_ClassId_ReferenceChannel OpenViBE::CIdentifier(0xEFA8E95B, 0x4F22551B)
|
||||
#define OVP_ClassId_ReferenceChannelDesc OpenViBE::CIdentifier(0x1873B151, 0x969DD4E4)
|
||||
#define OVP_ClassId_ChannelSelector OpenViBE::CIdentifier(0x39484563, 0x46386889)
|
||||
#define OVP_ClassId_ChannelSelectorDesc OpenViBE::CIdentifier(0x34893489, 0x44934897)
|
||||
#define OVP_ClassId_SimpleDSP OpenViBE::CIdentifier(0x00E26FA1, 0x1DBAB1B2)
|
||||
#define OVP_ClassId_SimpleDSPDesc OpenViBE::CIdentifier(0x00C44BFE, 0x76C9269E)
|
||||
#define OVP_ClassId_SignalAverage OpenViBE::CIdentifier(0x00642C4D, 0x5DF7E50A)
|
||||
#define OVP_ClassId_SignalAverageDesc OpenViBE::CIdentifier(0x007CDCE9, 0x16034F77)
|
||||
#define OVP_ClassId_SignalConcatenation OpenViBE::CIdentifier(0x6568D29B, 0x0D753CCA)
|
||||
#define OVP_ClassId_SignalConcatenationDesc OpenViBE::CIdentifier(0x3921BACD, 0x1E9546FE)
|
||||
#define OVP_ClassId_BoxAlgorithm_QuadraticForm OpenViBE::CIdentifier(0x54E73B81, 0x1AD356C6)
|
||||
#define OVP_ClassId_BoxAlgorithm_QuadraticFormDesc OpenViBE::CIdentifier(0x31C11856, 0x3E4F7B67)
|
||||
#define OVP_ClassId_BoxAlgorithm_EpochVariance OpenViBE::CIdentifier(0x335384EA, 0x88C917D9)
|
||||
#define OVP_ClassId_BoxAlgorithm_EpochVarianceDesc OpenViBE::CIdentifier(0xA15EAEC5, 0xAB0CE73D)
|
||||
#define OVP_ClassId_Algorithm_MatrixVariance OpenViBE::CIdentifier(0x7FEFDCA9, 0x816ED903)
|
||||
#define OVP_ClassId_Algorithm_MatrixVarianceDesc OpenViBE::CIdentifier(0xE405260B, 0x59EEFAE4)
|
||||
#define OVP_ClassId_Algorithm_HilbertTransform OpenViBE::CIdentifier(0x344B79DE, 0x89EAAABB)
|
||||
#define OVP_ClassId_Algorithm_HilbertTransformDesc OpenViBE::CIdentifier(0x8CAB236A, 0xA789800D)
|
||||
#define OVP_ClassId_BoxAlgorithm_Hilbert OpenViBE::CIdentifier(0x7878A47F, 0x9A8FE349)
|
||||
#define OVP_ClassId_BoxAlgorithm_HilbertDesc OpenViBE::CIdentifier(0x2DB54E2F, 0x435675EF)
|
||||
#define OVP_ClassId_Algorithm_ARBurgMethod OpenViBE::CIdentifier(0x3EC6A165, 0x2823A034)
|
||||
#define OVP_ClassId_Algorithm_ARBurgMethodDesc OpenViBE::CIdentifier(0xD7234DFF, 0x55447A14)
|
||||
#define OVP_ClassId_BoxAlgorithm_XDAWNSpatialFilterTrainer OpenViBE::CIdentifier(0xAE241F9F, 0x599FAD88)
|
||||
#define OVP_ClassId_BoxAlgorithm_XDAWNSpatialFilterTrainerDesc OpenViBE::CIdentifier(0x46FFAD13, 0x5F5C68CE)
|
||||
#define OVP_ClassId_BoxAlgorithm_IFFTbox OpenViBE::CIdentifier(0xD533E997, 0x4AFD2423)
|
||||
#define OVP_ClassId_BoxAlgorithm_IFFTboxDesc OpenViBE::CIdentifier(0xD533E997, 0x4AFD2423)
|
||||
#define OVP_ClassId_BoxAlgorithm_Matrix3dTo2d OpenViBE::CIdentifier(0x3ab2b81e, 0x73ef01a5)
|
||||
#define OVP_ClassId_BoxAlgorithm_Matrix3dTo2dDesc OpenViBE::CIdentifier(0xb9099590, 0xae33d758)
|
||||
|
||||
// Type definitions
|
||||
//---------------------------------------------------------------------------------------------------
|
||||
|
||||
#define OVP_TypeId_EpochAverageMethod OpenViBE::CIdentifier(0x6530BDB1, 0xD057BBFE)
|
||||
#define OVP_TypeId_CropMethod OpenViBE::CIdentifier(0xD0643F9E, 0x8E35FE0A)
|
||||
#define OVP_TypeId_SelectionMethod OpenViBE::CIdentifier(0x3BCF9E67, 0x0C23994D)
|
||||
#define OVP_TypeId_MatchMethod OpenViBE::CIdentifier(0x666F25E9, 0x3E5738D6)
|
||||
#define OVP_TypeId_DifferentialIntegralOperation OpenViBE::CIdentifier(0x6E6AD85D, 0x14FD203A)
|
||||
#define OVP_TypeId_WindowType OpenViBE::CIdentifier(0x332BBB80, 0xC212810A)
|
||||
#define OVP_TypeId_WaveletType OpenViBE::CIdentifier(0x393EAC3E, 0x793C0F1D)
|
||||
#define OVP_TypeId_WaveletLevel OpenViBE::CIdentifier(0xF80A2144, 0x6E692C51)
|
||||
|
||||
enum class EEpochAverageMethod { Moving, MovingImmediate, Block, Cumulative };
|
||||
|
||||
enum class ECropMethod { Min, Max, MinMax };
|
||||
|
||||
enum class ESelectionMethod { Select, Reject, Select_EEG };
|
||||
|
||||
enum class EMatchMethod { Name, Index, Smart };
|
||||
|
||||
enum class EDifferentialIntegralOperation { Differential, Integral };
|
||||
|
||||
enum class EWaveletType
|
||||
{
|
||||
Haar,
|
||||
Db1, Db2, Db3, Db4, Db5, Db6, Db7, Db8, Db9, Db10, Db11, Db12, Db13, Db14, Db15,
|
||||
Bior11, Bior13, Bior15, Bior22, Bior24, Bior26, Bior28, Bior31, Bior33, Bior35, Bior37, Bior39, Bior44, Bior55, Bior68,
|
||||
Coif1, Coif2, Coif3, Coif4, Coif5,
|
||||
Sym1, Sym2, Sym3, Sym4, Sym5, Sym6, Sym7, Sym8, Sym9, Sym10
|
||||
};
|
||||
|
||||
enum class EWaveletLevel { L1, L2, L3, L4, L5 };
|
||||
|
||||
// Global defines
|
||||
//---------------------------------------------------------------------------------------------------
|
||||
#ifdef TARGET_HAS_ThirdPartyOpenViBEPluginsGlobalDefines
|
||||
#include "ovp_global_defines.h"
|
||||
#endif // TARGET_HAS_ThirdPartyOpenViBEPluginsGlobalDefines
|
||||
|
||||
#define OVP_Value_CoupledStringSeparator '-'
|
||||
//#define OVP_Value_AllSelection '*'
|
||||
|
||||
#define OVP_Algorithm_MatrixAverage_InputParameterId_Matrix OpenViBE::CIdentifier(0x913E9C3B, 0x8A62F5E3)
|
||||
#define OVP_Algorithm_MatrixAverage_InputParameterId_MatrixCount OpenViBE::CIdentifier(0x08563191, 0xE78BB265)
|
||||
#define OVP_Algorithm_MatrixAverage_InputParameterId_AveragingMethod OpenViBE::CIdentifier(0xE63CD759, 0xB6ECF6B7)
|
||||
#define OVP_Algorithm_MatrixAverage_OutputParameterId_AveragedMatrix OpenViBE::CIdentifier(0x03CE5AE5, 0xBD9031E0)
|
||||
#define OVP_Algorithm_MatrixAverage_InputTriggerId_Reset OpenViBE::CIdentifier(0x670EC053, 0xADFE3F5C)
|
||||
#define OVP_Algorithm_MatrixAverage_InputTriggerId_FeedMatrix OpenViBE::CIdentifier(0x50B6EE87, 0xDC42E660)
|
||||
#define OVP_Algorithm_MatrixAverage_InputTriggerId_ForceAverage OpenViBE::CIdentifier(0xBF597839, 0xCD6039F0)
|
||||
#define OVP_Algorithm_MatrixAverage_OutputTriggerId_AveragePerformed OpenViBE::CIdentifier(0x2BFF029B, 0xD932A613)
|
||||
|
||||
#define OVP_Algorithm_StimulationBasedEpoching_InputParameterId_InputSignal OpenViBE::CIdentifier(0x0ED5C92B, 0xE16BEF25)
|
||||
#define OVP_Algorithm_StimulationBasedEpoching_InputParameterId_OffsetSampleCount OpenViBE::CIdentifier(0x7646CE65, 0xE128FC4E)
|
||||
#define OVP_Algorithm_StimulationBasedEpoching_OutputParameterId_OutputSignal OpenViBE::CIdentifier(0x00D331A2, 0xC13DF043)
|
||||
#define OVP_Algorithm_StimulationBasedEpoching_InputTriggerId_Reset OpenViBE::CIdentifier(0x6BA44128, 0x418CF901)
|
||||
#define OVP_Algorithm_StimulationBasedEpoching_InputTriggerId_PerformEpoching OpenViBE::CIdentifier(0xD05579B5, 0x2649A4B2)
|
||||
#define OVP_Algorithm_StimulationBasedEpoching_OutputTriggerId_EpochingDone OpenViBE::CIdentifier(0x755BC3FE, 0x24F7B50F)
|
||||
#define OVP_Algorithm_StimulationBasedEpoching_InputParameterId_EndTimeChunkToProcess OpenViBE::CIdentifier(0x8B552604, 0x10CD1F94)
|
||||
|
||||
#define OVP_Algorithm_MatrixVariance_InputParameterId_Matrix OpenViBE::CIdentifier(0x781F51CA, 0xE6E3B0B8)
|
||||
#define OVP_Algorithm_MatrixVariance_InputParameterId_MatrixCount OpenViBE::CIdentifier(0xE5103C63, 0x08D825E0)
|
||||
#define OVP_Algorithm_MatrixVariance_InputParameterId_AveragingMethod OpenViBE::CIdentifier(0x043A1BC4, 0x925D3CD6)
|
||||
#define OVP_Algorithm_MatrixVariance_InputParameterId_SignificanceLevel OpenViBE::CIdentifier(0x1E1065B2, 0x2CA32013)
|
||||
#define OVP_Algorithm_MatrixVariance_OutputParameterId_AveragedMatrix OpenViBE::CIdentifier(0x5CF66A73, 0xF5BBF0BF)
|
||||
#define OVP_Algorithm_MatrixVariance_OutputParameterId_Variance OpenViBE::CIdentifier(0x1BD67420, 0x587600E6)
|
||||
#define OVP_Algorithm_MatrixVariance_OutputParameterId_ConfidenceBound OpenViBE::CIdentifier(0x1E1065B2, 0x2CA32013)
|
||||
#define OVP_Algorithm_MatrixVariance_InputTriggerId_Reset OpenViBE::CIdentifier(0xD5C5EF91, 0xE1B1C4F4)
|
||||
#define OVP_Algorithm_MatrixVariance_InputTriggerId_FeedMatrix OpenViBE::CIdentifier(0xEBAEB213, 0xDD4735A0)
|
||||
#define OVP_Algorithm_MatrixVariance_InputTriggerId_ForceAverage OpenViBE::CIdentifier(0x344A52F5, 0x489DB439)
|
||||
#define OVP_Algorithm_MatrixVariance_OutputTriggerId_AveragePerformed OpenViBE::CIdentifier(0x2F9ECA0B, 0x8D3CA7BD)
|
||||
|
||||
#define OVP_Algorithm_ARBurgMethod_InputParameterId_Matrix OpenViBE::CIdentifier(0x36A69669, 0x3651271D)
|
||||
#define OVP_Algorithm_ARBurgMethod_OutputParameterId_Matrix OpenViBE::CIdentifier(0x55EF8C81, 0x178A51B2)
|
||||
#define OVP_Algorithm_ARBurgMethod_InputParameterId_UInteger OpenViBE::CIdentifier(0x33139BC1, 0x03D30D3B)
|
||||
#define OVP_Algorithm_ARBurgMethod_InputTriggerId_Initialize OpenViBE::CIdentifier(0xC27B06C6, 0xB8EB5F8D)
|
||||
#define OVP_Algorithm_ARBurgMethod_InputTriggerId_Process OpenViBE::CIdentifier(0xBEEBBE84, 0x4F14F8F8)
|
||||
#define OVP_Algorithm_ARBurgMethod_OutputTriggerId_ProcessDone OpenViBE::CIdentifier(0xA5AAD435, 0x9EC3DB80)
|
||||
|
||||
#define OVP_Algorithm_MagnitudeSquaredCoherence_InputParameterId_SegLength OpenViBE::CIdentifier(0xA4826743, 0x0FA27C06)
|
||||
#define OVP_Algorithm_MagnitudeSquaredCoherence_InputParameterId_Overlap OpenViBE::CIdentifier(0x527F8AEC, 0xA25F2EAB)
|
||||
#define OVP_Algorithm_MagnitudeSquaredCoherence_InputParameterId_Window OpenViBE::CIdentifier(0x0EA349EE, 0xB9DC95D0)
|
||||
#define OVP_Algorithm_MagnitudeSquaredCoherence_InputParameterId_Nfft OpenViBE::CIdentifier(0x7726C677, 0xE266C5A2)
|
||||
#define OVP_Algorithm_MagnitudeSquaredCoherence_OutputParameterId_OutputMatrixSpectrum OpenViBE::CIdentifier(0x331326BA, 0xA94CFC8A)
|
||||
#define OVP_Algorithm_MagnitudeSquaredCoherence_OutputParameterId_FreqVector OpenViBE::CIdentifier(0xD9FAA21C, 0x67D7C451)
|
||||
|
||||
#define OVP_Algorithm_HilbertTransform_InputParameterId_Matrix OpenViBE::CIdentifier(0xC117CE9A, 0x3FFCB156)
|
||||
#define OVP_Algorithm_HilbertTransform_OutputParameterId_HilbertMatrix OpenViBE::CIdentifier(0xDAE13CB8, 0xEFF82E69)
|
||||
#define OVP_Algorithm_HilbertTransform_OutputParameterId_EnvelopeMatrix OpenViBE::CIdentifier(0x9D0A023A, 0x7690C48E)
|
||||
#define OVP_Algorithm_HilbertTransform_OutputParameterId_PhaseMatrix OpenViBE::CIdentifier(0x495B55E2, 0x8CAAC08E)
|
||||
#define OVP_Algorithm_HilbertTransform_InputTriggerId_Initialize OpenViBE::CIdentifier(0xE4B3CB4A, 0xF0121A20)
|
||||
#define OVP_Algorithm_HilbertTransform_InputTriggerId_Process OpenViBE::CIdentifier(0xC3DC087D, 0x4AAFC1F0)
|
||||
#define OVP_Algorithm_HilbertTransform_OutputTriggerId_ProcessDone OpenViBE::CIdentifier(0xB0B2A2DD, 0x73529B46)
|
||||
|
||||
#define OVP_TypeId_Connectivity_Metric OpenViBE::CIdentifier(0x9188339d, 0x5da83a84)
|
||||
#define OV_TypeId_ConnectivityMeasure_WindowMethod OpenViBE::CIdentifier(0x8815bfa7, 0x557b102f)
|
||||
|
||||
#define OV_AttributeId_Box_FlagIsUnstable OpenViBE::CIdentifier(0x666FFFFF, 0x666FFFFF)
|
||||
Executable
+167
@@ -0,0 +1,167 @@
|
||||
#include "box-algorithms/basic/ovpCBoxAlgorithmMatrixTranspose.h"
|
||||
#include "box-algorithms/basic/ovpCBoxAlgorithmDifferentialIntegral.h"
|
||||
|
||||
#include "box-algorithms/connectivity/CBoxAlgorithmConnectivityMeasure.hpp"
|
||||
|
||||
#include "box-algorithms/basic/ovpCBoxAlgorithmHilbert.h"
|
||||
#include "algorithms/basic/ovpCHilbertTransform.h"
|
||||
|
||||
#include "box-algorithms/ovpCBoxAlgorithmQuadraticForm.h"
|
||||
|
||||
#include "box-algorithms/filters/ovpCBoxAlgorithmXDAWNSpatialFilterTrainer.h"
|
||||
|
||||
#include "box-algorithms/basic/ovpCBoxAlgorithmIFFTbox.h"
|
||||
|
||||
#include "algorithms/basic/ovpCAlgorithmARBurgMethod.h"
|
||||
#include "box-algorithms/basic/ovpCBoxAlgorithmARCoefficients.h"
|
||||
|
||||
#include "algorithms/basic/ovpCMatrixVariance.h"
|
||||
#include "box-algorithms/basic/ovpCBoxAlgorithmEpochVariance.h"
|
||||
#include "box-algorithms/basic/ovpCBoxAlgorithmERSPAverage.h"
|
||||
|
||||
//#include "box-algorithms/basic/ovpCBoxAlgorithmNull.h"
|
||||
|
||||
#include "box-algorithms/ovpCBoxAlgorithmEOG_Denoising.h"
|
||||
#include "box-algorithms/ovpCBoxAlgorithmEOG_Denoising_Calibration.h"
|
||||
#include "box-algorithms/ovpCBoxAlgorithmDiscreteWaveletTransform.h"
|
||||
#include "box-algorithms/ovpCBoxAlgorithmInverse_DWT.h"
|
||||
|
||||
#include "box-algorithms/CBoxAlgorithmMatrix3dTo2d.hpp"
|
||||
|
||||
namespace OpenViBE {
|
||||
namespace Plugins {
|
||||
namespace SignalProcessing {
|
||||
|
||||
OVP_Declare_Begin()
|
||||
//*********** Boxes ***********
|
||||
OVP_Declare_New(CBoxAlgorithmDifferentialIntegralDesc)
|
||||
OVP_Declare_New(CBoxAlgorithmMatrixTransposeDesc)
|
||||
//OVP_Declare_New(CBoxAlgorithmNullDesc)
|
||||
OVP_Declare_New(CBoxAlgorithmERSPAverageDesc)
|
||||
OVP_Declare_New(CBoxAlgorithmQuadraticFormDesc)
|
||||
OVP_Declare_New(CEpochVarianceDesc);
|
||||
OVP_Declare_New(CBoxAlgorithmMatrix3dTo2dDesc)
|
||||
|
||||
#if defined TARGET_HAS_ThirdPartyEIGEN
|
||||
OVP_Declare_New(CBoxAlgorithmARCoefficientsDesc);
|
||||
OVP_Declare_New(CAlgorithmARBurgMethodDesc);
|
||||
OVP_Declare_New(CAlgorithmHilbertTransformDesc)
|
||||
OVP_Declare_New(CBoxAlgorithmConnectivityMeasureDesc)
|
||||
OVP_Declare_New(CBoxAlgorithmEOG_DenoisingDesc);
|
||||
OVP_Declare_New(CBoxAlgorithmEOG_Denoising_CalibrationDesc);
|
||||
OVP_Declare_New(CBoxAlgorithmHilbertDesc)
|
||||
#endif
|
||||
|
||||
#if defined TARGET_HAS_ThirdPartyITPP
|
||||
OVP_Declare_New(CBoxAlgorithmXDAWNSpatialFilterTrainerDesc)
|
||||
OVP_Declare_New(CMatrixVarianceDesc)
|
||||
OVP_Declare_New(CBoxAlgorithmIFFTboxDesc)
|
||||
#endif // TARGET_HAS_ThirdPartyITPP
|
||||
|
||||
#if defined(TARGET_HAS_ThirdPartyFFTW3)
|
||||
OVP_Declare_New(CBoxAlgorithmDiscreteWaveletTransformDesc)
|
||||
OVP_Declare_New(CBoxAlgorithmInverse_DWTDesc)
|
||||
|
||||
//*********** Enumeration ***********
|
||||
context.getTypeManager().registerEnumerationType(OVP_TypeId_WaveletType, "Wavelet type");
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "haar", size_t(EWaveletType::Haar));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "db1", size_t(EWaveletType::Db1));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "db2", size_t(EWaveletType::Db2));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "db3", size_t(EWaveletType::Db3));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "db4", size_t(EWaveletType::Db4));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "db5", size_t(EWaveletType::Db5));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "db6", size_t(EWaveletType::Db6));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "db7", size_t(EWaveletType::Db7));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "db8", size_t(EWaveletType::Db8));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "db9", size_t(EWaveletType::Db9));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "db10", size_t(EWaveletType::Db10));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "db11", size_t(EWaveletType::Db11));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "db12", size_t(EWaveletType::Db12));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "db13", size_t(EWaveletType::Db13));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "db14", size_t(EWaveletType::Db14));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "db15", size_t(EWaveletType::Db15));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "bior1.1", size_t(EWaveletType::Bior11));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "bior1.3", size_t(EWaveletType::Bior13));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "bior1.5", size_t(EWaveletType::Bior15));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "bior2.2", size_t(EWaveletType::Bior22));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "bior2.4", size_t(EWaveletType::Bior24));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "bior2.6", size_t(EWaveletType::Bior26));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "bior2.8", size_t(EWaveletType::Bior28));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "bior3.1", size_t(EWaveletType::Bior31));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "bior3.3", size_t(EWaveletType::Bior33));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "bior3.5", size_t(EWaveletType::Bior35));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "bior3.7", size_t(EWaveletType::Bior37));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "bior3.9", size_t(EWaveletType::Bior39));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "bior4.4", size_t(EWaveletType::Bior44));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "bior5.5", size_t(EWaveletType::Bior55));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "bior6.8", size_t(EWaveletType::Bior68));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "coif1", size_t(EWaveletType::Coif1));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "coif2", size_t(EWaveletType::Coif2));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "coif3", size_t(EWaveletType::Coif3));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "coif4", size_t(EWaveletType::Coif4));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "coif5", size_t(EWaveletType::Coif5));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "sym1", size_t(EWaveletType::Sym1));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "sym2", size_t(EWaveletType::Sym2));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "sym3", size_t(EWaveletType::Sym3));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "sym4", size_t(EWaveletType::Sym4));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "sym5", size_t(EWaveletType::Sym5));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "sym6", size_t(EWaveletType::Sym6));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "sym7", size_t(EWaveletType::Sym7));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "sym8", size_t(EWaveletType::Sym8));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "sym9", size_t(EWaveletType::Sym9));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletType, "sym10", size_t(EWaveletType::Sym10));
|
||||
|
||||
context.getTypeManager().registerEnumerationType(OVP_TypeId_WaveletLevel, "Wavelet decomposition levels");
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletLevel, "1", size_t(EWaveletLevel::L1));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletLevel, "2", size_t(EWaveletLevel::L2));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletLevel, "3", size_t(EWaveletLevel::L3));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletLevel, "4", size_t(EWaveletLevel::L4));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_WaveletLevel, "5", size_t(EWaveletLevel::L5));
|
||||
#endif
|
||||
context.getTypeManager().registerEnumerationEntry(OV_TypeId_BoxAlgorithmFlag, OV_AttributeId_Box_FlagIsUnstable.toString(),
|
||||
OV_AttributeId_Box_FlagIsUnstable.id());
|
||||
|
||||
context.getTypeManager().registerEnumerationType(OVP_TypeId_EpochAverageMethod, "Epoch Average method");
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_EpochAverageMethod, "Moving epoch average", size_t(EEpochAverageMethod::Moving));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_EpochAverageMethod, "Moving epoch average (Immediate)",
|
||||
size_t(EEpochAverageMethod::MovingImmediate));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_EpochAverageMethod, "Epoch block average", size_t(EEpochAverageMethod::Block));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_EpochAverageMethod, "Cumulative average", size_t(EEpochAverageMethod::Cumulative));
|
||||
|
||||
context.getTypeManager().registerEnumerationType(OVP_TypeId_CropMethod, "Crop method");
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_CropMethod, "Min", size_t(ECropMethod::Min));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_CropMethod, "Max", size_t(ECropMethod::Max));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_CropMethod, "Min/Max", size_t(ECropMethod::MinMax));
|
||||
|
||||
|
||||
context.getTypeManager().registerEnumerationType(OVP_TypeId_SelectionMethod, "Selection method");
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_SelectionMethod, "Select", size_t(ESelectionMethod::Select));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_SelectionMethod, "Reject", size_t(ESelectionMethod::Reject));
|
||||
|
||||
context.getTypeManager().registerEnumerationType(OVP_TypeId_MatchMethod, "Match method");
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_MatchMethod, "Name", size_t(EMatchMethod::Name));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_MatchMethod, "Index", size_t(EMatchMethod::Index));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_MatchMethod, "Smart", size_t(EMatchMethod::Smart));
|
||||
|
||||
context.getTypeManager().registerEnumerationType(OVP_TypeId_DifferentialIntegralOperation, "Differential/Integral select");
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_DifferentialIntegralOperation, "Differential",
|
||||
size_t(EDifferentialIntegralOperation::Differential));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_DifferentialIntegralOperation, "Integral", size_t(EDifferentialIntegralOperation::Integral));
|
||||
|
||||
#if defined(TARGET_HAS_ThirdPartyEIGEN)
|
||||
context.getTypeManager().registerEnumerationType(OV_TypeId_ConnectivityMeasure_WindowMethod, "Welch Window method");
|
||||
context.getTypeManager().registerEnumerationEntry(OV_TypeId_ConnectivityMeasure_WindowMethod, "Hamming", size_t(EConnectWindowMethod::Hamming));
|
||||
context.getTypeManager().registerEnumerationEntry(OV_TypeId_ConnectivityMeasure_WindowMethod, "Hann", size_t(EConnectWindowMethod::Hann));
|
||||
context.getTypeManager().registerEnumerationEntry(OV_TypeId_ConnectivityMeasure_WindowMethod, "Welch", size_t(EConnectWindowMethod::Welch));
|
||||
|
||||
context.getTypeManager().registerEnumerationType(OVP_TypeId_Connectivity_Metric, "Metric method");
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_Connectivity_Metric, toString(EConnectMetric::Coherence).c_str(), size_t(EConnectMetric::Coherence));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_Connectivity_Metric, toString(EConnectMetric::MagnitudeSquaredCoherence).c_str(), size_t(EConnectMetric::MagnitudeSquaredCoherence));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_Connectivity_Metric, toString(EConnectMetric::ImaginaryCoherence).c_str(), size_t(EConnectMetric::ImaginaryCoherence));
|
||||
context.getTypeManager().registerEnumerationEntry(OVP_TypeId_Connectivity_Metric, toString(EConnectMetric::AbsImaginaryCoherence).c_str(), size_t(EConnectMetric::AbsImaginaryCoherence));
|
||||
#endif
|
||||
OVP_Declare_End()
|
||||
|
||||
} // namespace SignalProcessing
|
||||
} // namespace Plugins
|
||||
} // namespace OpenViBE
|
||||
+5
@@ -0,0 +1,5 @@
|
||||
/ARFeatures.csv
|
||||
/envelope_HilbertTransform.csv
|
||||
/hilbert_HilbertTransform.csv
|
||||
/phase_HilbertTransform.csv
|
||||
/PhaseLockingValue.csv
|
||||
+9
@@ -0,0 +1,9 @@
|
||||
Time (s);Channel 1
|
||||
0.0000000000e+000;1.0000000000e+000
|
||||
0.0000000000e+000;-1.3499924204e+000
|
||||
0.0000000000e+000;7.1548116977e-001
|
||||
0.0000000000e+000;-4.8823484546e-001
|
||||
0.0000000000e+000;1.3773934216e-001
|
||||
0.0000000000e+000;2.4122886822e-001
|
||||
0.0000000000e+000;-4.1646742087e-001
|
||||
0.0000000000e+000;1.6024997233e-001
|
||||
|
+534
@@ -0,0 +1,534 @@
|
||||
<OpenViBE-Scenario>
|
||||
<FormatVersion>2</FormatVersion>
|
||||
<Creator>OpenViBE Designer</Creator>
|
||||
<CreatorVersion>2.2.0</CreatorVersion>
|
||||
<Settings></Settings>
|
||||
<Inputs></Inputs>
|
||||
<Outputs></Outputs>
|
||||
<Boxes>
|
||||
<Box>
|
||||
<Identifier>(0x00000294, 0x00001670)</Identifier>
|
||||
<Name>Channel Selector</Name>
|
||||
<AlgorithmClassIdentifier>(0x361722e8, 0x311574e8)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Input signal</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Output signal</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x79a9edeb, 0x245d83fc)</TypeIdentifier>
|
||||
<Name>Channel List</Name>
|
||||
<DefaultValue>:</DefaultValue>
|
||||
<Value>1</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x3bcf9e67, 0x0c23994d)</TypeIdentifier>
|
||||
<Name>Action</Name>
|
||||
<DefaultValue>Select</DefaultValue>
|
||||
<Value>Select</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x666f25e9, 0x3e5738d6)</TypeIdentifier>
|
||||
<Name>Channel Matching Method</Name>
|
||||
<DefaultValue>Smart</DefaultValue>
|
||||
<Value>Smart</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>112</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>304</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x30a4e5c9, 0x83502953)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x277826e1, 0xa30a3bd0)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x527ad68d, 0x16d746a0)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x01dcd6d2)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>3</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x00000344, 0x0000087c)</Identifier>
|
||||
<Name>Clock stimulator</Name>
|
||||
<AlgorithmClassIdentifier>(0x4f756d3f, 0x29ff0b96)</AlgorithmClassIdentifier>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
|
||||
<Name>Generated stimulations</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Interstimulation interval (in sec)</Name>
|
||||
<DefaultValue>1.0</DefaultValue>
|
||||
<Value>1.05</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2c132d6e, 0x44ab0d97)</TypeIdentifier>
|
||||
<Name>Stimulation</Name>
|
||||
<DefaultValue>OVTK_StimulationId_Label_00</DefaultValue>
|
||||
<Value>OVTK_StimulationId_Label_00</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>144</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>496</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x27b3ee3c, 0xc50527e6)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x018792f9)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>2</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x000009d6, 0x0000143d)</Identifier>
|
||||
<Name>AutoRegressive Coefficients</Name>
|
||||
<AlgorithmClassIdentifier>(0xbaadc2f3, 0xb556a07b)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>EEG Signal</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x544a003e, 0x6dcba5f6)</TypeIdentifier>
|
||||
<Name>AR Features</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
|
||||
<Name>Order</Name>
|
||||
<DefaultValue>1</DefaultValue>
|
||||
<Value>7</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>272</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>304</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x316b3b58, 0xa4f4e384)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x020a8c7a)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x00002d56, 0x00007142)</Identifier>
|
||||
<Name>Player Controller</Name>
|
||||
<AlgorithmClassIdentifier>(0x5f426dce, 0x08456e13)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
|
||||
<Name>Stimulations</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2c132d6e, 0x44ab0d97)</TypeIdentifier>
|
||||
<Name>Stimulation name</Name>
|
||||
<DefaultValue>OVTK_StimulationId_Label_00</DefaultValue>
|
||||
<Value>OVTK_StimulationId_Label_00</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0xcc14d8d6, 0xf27ecb73)</TypeIdentifier>
|
||||
<Name>Action to perform</Name>
|
||||
<DefaultValue>Pause</DefaultValue>
|
||||
<Value>Stop</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>208</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>496</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x568d148e, 0x650792b3)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x01b260d4)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>2</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x00003560, 0x00001894)</Identifier>
|
||||
<Name>Time based epoching</Name>
|
||||
<AlgorithmClassIdentifier>(0x00777fa0, 0x5dc3f560)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Input signal</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Epoched signal 1</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Epoch 1 duration (in sec)</Name>
|
||||
<DefaultValue>1</DefaultValue>
|
||||
<Value>0.5</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Epoch 1 intervals (in sec)</Name>
|
||||
<DefaultValue>0.5</DefaultValue>
|
||||
<Value>1.5</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x17ee7c08, 0x94c14893)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>208</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>304</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0xc5ff41e9, 0xccc59a01)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x05bd1106)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>2</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x0000566f, 0x00003f53)</Identifier>
|
||||
<Name>GDF file reader</Name>
|
||||
<AlgorithmClassIdentifier>(0x3eeb1264, 0x4edfbd9a)</AlgorithmClassIdentifier>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x403488e7, 0x565d70b6)</TypeIdentifier>
|
||||
<Name>Experiment information</Name>
|
||||
</Output>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>EEG stream</Name>
|
||||
</Output>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
|
||||
<Name>Stimulations</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x330306dd, 0x74a95f98)</TypeIdentifier>
|
||||
<Name>Filename</Name>
|
||||
<DefaultValue></DefaultValue>
|
||||
<Value>${Path_Data}/scenarios/signals/real-hand-movements.gdf</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
|
||||
<Name>Samples per buffer</Name>
|
||||
<DefaultValue>32</DefaultValue>
|
||||
<Value>32</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Subtract physical minimum</Name>
|
||||
<DefaultValue>False</DefaultValue>
|
||||
<Value>False</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>31</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>304</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x78b8b69d, 0x27afe678)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x0144bf9d)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
||||
<Value>3</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>3</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x00007f36, 0x00004a0e)</Identifier>
|
||||
<Name>CSV File Writer</Name>
|
||||
<AlgorithmClassIdentifier>(0x2c9312f1, 0x2d6613e5)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x544a003e, 0x6dcba5f6)</TypeIdentifier>
|
||||
<Name>Streamed matrix</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Settings>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x330306dd, 0x74a95f98)</TypeIdentifier>
|
||||
<Name>Filename</Name>
|
||||
<DefaultValue>record-[$core{date}-$core{time}].csv</DefaultValue>
|
||||
<Value>${Player_ScenarioDirectory}/ARFeatures.csv</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x79a9edeb, 0x245d83fc)</TypeIdentifier>
|
||||
<Name>Column separator</Name>
|
||||
<DefaultValue>;</DefaultValue>
|
||||
<Value>;</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
|
||||
<Name>Precision</Name>
|
||||
<DefaultValue>10</DefaultValue>
|
||||
<Value>10</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>336</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>304</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0x229d1207, 0xebac8ab0)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x527ad68d, 0x16d746a0)</Identifier>
|
||||
<Value></Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
|
||||
<Value>(0x00000000, 0x00eea8eb)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>3</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
</Boxes>
|
||||
<Links>
|
||||
<Link>
|
||||
<Identifier>(0x000007ef, 0x0000158b)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x0000566f, 0x00003f53)</BoxIdentifier>
|
||||
<BoxOutputIndex>1</BoxOutputIndex>
|
||||
</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x00000294, 0x00001670)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
||||
</Target>
|
||||
</Link>
|
||||
<Link>
|
||||
<Identifier>(0x00001962, 0x00003ae4)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x00003560, 0x00001894)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
||||
</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x000009d6, 0x0000143d)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
||||
</Target>
|
||||
</Link>
|
||||
<Link>
|
||||
<Identifier>(0x0000207e, 0x00001215)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x00000294, 0x00001670)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
||||
</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x00003560, 0x00001894)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
||||
</Target>
|
||||
</Link>
|
||||
<Link>
|
||||
<Identifier>(0x00005ea0, 0x00005581)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x00000344, 0x0000087c)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
||||
</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x00002d56, 0x00007142)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
||||
</Target>
|
||||
</Link>
|
||||
<Link>
|
||||
<Identifier>(0x0000631a, 0x000038c3)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x000009d6, 0x0000143d)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
||||
</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x00007f36, 0x00004a0e)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
||||
</Target>
|
||||
</Link>
|
||||
</Links>
|
||||
<Comments></Comments>
|
||||
<Metadata>
|
||||
<Entry>
|
||||
<Identifier>(0x0000775c, 0x000078ff)</Identifier>
|
||||
<Type>(0x3bcce5d2, 0x43f2d968)</Type>
|
||||
<Data>[{"boxIdentifier":"(0xffffffff, 0xffffffff)","childCount":1,"height":1,"identifier":"(0x000044f4, 0x00002c81)","name":"Default window","parentIdentifier":"(0xffffffff, 0xffffffff)","type":1,"width":1},{"boxIdentifier":"(0xffffffff, 0xffffffff)","childCount":1,"identifier":"(0x00001207, 0x00004ca2)","index":0,"name":"Default tab","parentIdentifier":"(0x000044f4, 0x00002c81)","type":2},{"boxIdentifier":"(0xffffffff, 0xffffffff)","childCount":0,"identifier":"(0x00005c3e, 0x00002c50)","index":0,"name":"Empty","parentIdentifier":"(0x00001207, 0x00004ca2)","type":0}]</Data>
|
||||
</Entry>
|
||||
</Metadata>
|
||||
</OpenViBE-Scenario>
|
||||
+58
@@ -0,0 +1,58 @@
|
||||
PROJECT(test_thresholdDataComparison)
|
||||
|
||||
IF(WIN32)
|
||||
ADD_DEFINITIONS(-DTARGET_OS_Windows)
|
||||
ENDIF(WIN32)
|
||||
IF(UNIX)
|
||||
ADD_DEFINITIONS(-DTARGET_OS_Linux)
|
||||
ENDIF(UNIX)
|
||||
ADD_DEFINITIONS(-D_CRT_SECURE_NO_DEPRECATE)
|
||||
ADD_DEFINITIONS(-DTARGET_ARCHITECTURE_i386)
|
||||
|
||||
INCLUDE_DIRECTORIES(../src)
|
||||
ADD_EXECUTABLE(${PROJECT_NAME} test_thresholdDataComparison.cpp)
|
||||
SET_PROPERTY(TARGET ${PROJECT_NAME} PROPERTY FOLDER ${TESTS_FOLDER}) # Place project in folder unit-test (for some IDE)
|
||||
|
||||
INCLUDE("FindOpenViBE")
|
||||
|
||||
# Unfortunately we need to install the tests as any application to find .dll/.so files
|
||||
# on both Windows and Linux.
|
||||
OV_INSTALL_LAUNCH_SCRIPT(SCRIPT_PREFIX ${PROJECT_NAME} EXECUTABLE_NAME ${PROJECT_NAME})
|
||||
INSTALL(TARGETS ${PROJECT_NAME}
|
||||
RUNTIME DESTINATION ${DIST_BINDIR}
|
||||
LIBRARY DESTINATION ${DIST_LIBDIR}
|
||||
ARCHIVE DESTINATION ${DIST_LIBDIR})
|
||||
|
||||
PROJECT(test_eigen)
|
||||
|
||||
IF(WIN32)
|
||||
ADD_DEFINITIONS(-DTARGET_OS_Windows)
|
||||
ENDIF(WIN32)
|
||||
IF(UNIX)
|
||||
ADD_DEFINITIONS(-DTARGET_OS_Linux)
|
||||
ENDIF(UNIX)
|
||||
ADD_DEFINITIONS(-D_CRT_SECURE_NO_DEPRECATE)
|
||||
ADD_DEFINITIONS(-DTARGET_ARCHITECTURE_i386)
|
||||
|
||||
INCLUDE_DIRECTORIES(../src)
|
||||
|
||||
ADD_EXECUTABLE(${PROJECT_NAME} test_eigen.cpp)
|
||||
SET_PROPERTY(TARGET ${PROJECT_NAME} PROPERTY FOLDER ${TESTS_FOLDER}) # Place project in folder unit-test (for some IDE)
|
||||
|
||||
INCLUDE("FindOpenViBE")
|
||||
INCLUDE("FindOpenViBEModuleSystem")
|
||||
INCLUDE("FindThirdPartyEigen")
|
||||
|
||||
IF(WIN32)
|
||||
# Since cert, getting timeBeginPeriod() linker issues without this
|
||||
TARGET_LINK_LIBRARIES(${PROJECT_NAME} winmm)
|
||||
ENDIF(WIN32)
|
||||
|
||||
# Unfortunately we need to install the tests as any application to find .dll/.so files
|
||||
# on both Windows and Linux.
|
||||
OV_INSTALL_LAUNCH_SCRIPT(SCRIPT_PREFIX ${PROJECT_NAME} EXECUTABLE_NAME ${PROJECT_NAME})
|
||||
INSTALL(TARGETS ${PROJECT_NAME}
|
||||
RUNTIME DESTINATION ${DIST_BINDIR}
|
||||
LIBRARY DESTINATION ${DIST_LIBDIR}
|
||||
ARCHIVE DESTINATION ${DIST_LIBDIR})
|
||||
|
||||
+125
@@ -0,0 +1,125 @@
|
||||
# Basic Template Test for automatic run a scenario that produce a file to be compared to a reference file
|
||||
# You need to set the name of the test according to name of scenario file and reference file
|
||||
|
||||
# Test Hilbert transform Box
|
||||
|
||||
SET(TEST_NAME "HilbertTransform")
|
||||
SET(SCENARIO_TO_TEST "${TEST_NAME}.xml")
|
||||
|
||||
# These three tests are not yet working correctly...
|
||||
# SET(SUBTEST1_NAME "hilbert")
|
||||
# SET(SUBTEST2_NAME "phase")
|
||||
# SET(SUBTEST3_NAME "envelope")
|
||||
|
||||
IF(WIN32)
|
||||
SET(EXT cmd)
|
||||
SET(OS_FLAGS "--no-pause")
|
||||
ELSE()
|
||||
SET(EXT sh)
|
||||
SET(OS_FLAGS "")
|
||||
ENDIF()
|
||||
|
||||
ADD_TEST(clean_${TEST_NAME} "${CMAKE_COMMAND}" "-E" "remove" "-f" "${SUBTEST1_NAME}.csv" "${SUBTEST2_NAME}.csv" "${SUBTEST3_NAME}.csv")
|
||||
ADD_TEST(run_${TEST_NAME} "$ENV{OV_BINARY_PATH}/openvibe-designer.${EXT}" ${OS_FLAGS} "--no-session-management" "--invisible" "--play" ${SCENARIO_TO_TEST})
|
||||
# ADD_TEST(compare_${SUBTEST1_NAME} "$ENV{OV_BINARY_PATH}/test_thresholdDataComparison.${EXT}" ${OS_FLAGS} "${SUBTEST1_NAME}_${TEST_NAME}.csv" "${SUBTEST1_NAME}_${TEST_NAME}.ref.csv" 0.1)
|
||||
# ADD_TEST(compare_${SUBTEST2_NAME} "$ENV{OV_BINARY_PATH}/test_thresholdDataComparison.${EXT}" ${OS_FLAGS} "${SUBTEST2_NAME}_${TEST_NAME}.csv" "${SUBTEST2_NAME}_${TEST_NAME}.ref.csv" 0.1)
|
||||
# ADD_TEST(compare_${SUBTEST3_NAME} "$ENV{OV_BINARY_PATH}/test_thresholdDataComparison.${EXT}" ${OS_FLAGS} "${SUBTEST3_NAME}_${TEST_NAME}.csv" "${SUBTEST3_NAME}_${TEST_NAME}.ref.csv" 0.1)
|
||||
|
||||
## add some properties that help to debug
|
||||
SET_TESTS_PROPERTIES(run_${TEST_NAME} PROPERTIES ATTACHED_FILES_ON_FAIL ${OV_LOGFILE})
|
||||
|
||||
|
||||
#SET_TESTS_PROPERTIES(compare_${SUBTEST1_NAME} PROPERTIES ATTACHED_FILES_ON_FAIL "${SUBTEST1_NAME}.csv")
|
||||
#SET_TESTS_PROPERTIES(compare_${SUBTEST2_NAME} PROPERTIES ATTACHED_FILES_ON_FAIL "${SUBTEST2_NAME}.csv")
|
||||
#SET_TESTS_PROPERTIES(compare_${SUBTEST3_NAME} PROPERTIES ATTACHED_FILES_ON_FAIL "${SUBTEST3_NAME}.csv")
|
||||
#SET_TESTS_PROPERTIES(compare_${SUBTEST1_NAME} compare_${SUBTEST2_NAME} compare_${SUBTEST3_NAME} PROPERTIES DEPENDS run_${TEST_NAME})
|
||||
#SET_TESTS_PROPERTIES(run_${TEST_NAME} PROPERTIES DEPENDS clean_${TEST_NAME})
|
||||
|
||||
|
||||
|
||||
# Test Connectivity Box - PLV algorithm
|
||||
|
||||
#SET(TEST_NAME "PhaseLockingValue")
|
||||
#SET(SCENARIO_TO_TEST "${TEST_NAME}.xml")
|
||||
|
||||
#ADD_TEST(clean_${TEST_NAME} "${CMAKE_COMMAND}" "-E" "remove" "-f" "${TEST_NAME}.csv")
|
||||
#ADD_TEST(run_${TEST_NAME} "$ENV{OV_BINARY_PATH}/openvibe-designer.${EXT}" ${OS_FLAGS} "--no-session-management" "--invisible" "--play" ${SCENARIO_TO_TEST})
|
||||
#ADD_TEST(compare_${TEST_NAME} "$ENV{OV_BINARY_PATH}/test_thresholdDataComparison.${EXT}" ${OS_FLAGS} "${TEST_NAME}.csv" "${TEST_NAME}.ref.csv" 0.1)
|
||||
|
||||
## add some properties that help to debug
|
||||
#SET_TESTS_PROPERTIES(run_${TEST_NAME} PROPERTIES ATTACHED_FILES_ON_FAIL ${OV_LOGFILE})
|
||||
|
||||
|
||||
#SET_TESTS_PROPERTIES(compare_${TEST_NAME} PROPERTIES ATTACHED_FILES_ON_FAIL "${TEST_NAME}.csv")
|
||||
#SET_TESTS_PROPERTIES(compare_${TEST_NAME} PROPERTIES DEPENDS run_${TEST_NAME})
|
||||
#SET_TESTS_PROPERTIES(run_${TEST_NAME} PROPERTIES DEPENDS clean_${TEST_NAME})
|
||||
|
||||
|
||||
|
||||
# Test Auto-Regressive model
|
||||
|
||||
SET(TEST_NAME "ARFeatures")
|
||||
SET(SCENARIO_TO_TEST "${TEST_NAME}.xml")
|
||||
|
||||
ADD_TEST(clean_${TEST_NAME} "${CMAKE_COMMAND}" "-E" "remove" "-f" "${TEST_NAME}.csv")
|
||||
ADD_TEST(run_${TEST_NAME} "$ENV{OV_BINARY_PATH}/openvibe-designer.${EXT}" ${OS_FLAGS} "--no-session-management" "--invisible" "--play" ${SCENARIO_TO_TEST})
|
||||
ADD_TEST(compare_${TEST_NAME} "$ENV{OV_BINARY_PATH}/test_thresholdDataComparison.${EXT}" ${OS_FLAGS} "${TEST_NAME}.csv" "${TEST_NAME}.ref.csv" 0.1)
|
||||
|
||||
## add some properties that help to debug
|
||||
SET_TESTS_PROPERTIES(run_${TEST_NAME} PROPERTIES ATTACHED_FILES_ON_FAIL ${OV_LOGFILE})
|
||||
|
||||
SET_TESTS_PROPERTIES(compare_${TEST_NAME} PROPERTIES ATTACHED_FILES_ON_FAIL "${TEST_NAME}.csv")
|
||||
SET_TESTS_PROPERTIES(compare_${TEST_NAME} PROPERTIES DEPENDS run_${TEST_NAME})
|
||||
SET_TESTS_PROPERTIES(run_${TEST_NAME} PROPERTIES DEPENDS clean_${TEST_NAME})
|
||||
|
||||
|
||||
### Do not enable the commented out sikuli tests unless you
|
||||
### or your lab commits to keep them passing in the long term.
|
||||
#FIND_PROGRAM(SIKULI NAMES sikuli-ide)
|
||||
#IF(SIKULI)
|
||||
# IF(UNIX)
|
||||
# ADD_TEST(sikuli_crop "${SIKULI}" -t testCrop.UNIX.sikuli)
|
||||
# ENDIF(UNIX)
|
||||
#ENDIF(SIKULI)
|
||||
|
||||
# Test Regularized CSP
|
||||
# @TODO There is a problem with this box
|
||||
|
||||
#SET(TEST_SCENARIOS "RegularizedCSP_None" "RegularizedCSP_Tikhonov" "RegularizedCSP_Shrink" "RegularizedCSP_Both")
|
||||
#SET(TEST_SHRINKS 0.0 0.0 0.9 0.5)
|
||||
#SET(TEST_TIKHONOVS 0.0 0.9 0.0 0.5)
|
||||
#SET(TEST_THRESHOLDS 40 70 70 70)
|
||||
#
|
||||
#FOREACH(TEST_NAME ${TEST_SCENARIOS})
|
||||
#
|
||||
# ADD_TEST(clean_${TEST_NAME} "${CMAKE_COMMAND}" "-E" "remove" "-f" ${OV_LOGFILE} output-${TEST_NAME}.txt)
|
||||
#
|
||||
# LIST(GET TEST_SHRINKS 0 PARAM_SHRINK)
|
||||
# LIST(GET TEST_TIKHONOVS 0 PARAM_TIKHONOV)
|
||||
# LIST(GET TEST_THRESHOLDS 0 COMPARE_THRESHOLD)
|
||||
#
|
||||
# ADD_TEST(run_${TEST_NAME}_Train "$ENV{OV_BINARY_PATH}/openvibe-designer.${EXT}" ${OS_FLAGS} "--invisible" "--no-session-management" --define Plugin_Classification_RandomizeKFoldTestData true --random-seed 123 --define TEST_TIKHONOV ${PARAM_TIKHONOV} --define TEST_SHRINK ${PARAM_SHRINK} --define TEST_FILTER output-${TEST_NAME}.txt "--play-fast" "test-regularizedcsp-train.xml")
|
||||
#
|
||||
# ADD_TEST(run_${TEST_NAME}_Test "$ENV{OV_BINARY_PATH}/openvibe-designer.${EXT}" ${OS_FLAGS} "--invisible" "--no-session-management" --define Plugin_Classification_RandomizeKFoldTestData true --random-seed 456 --define TEST_FILTER output-${TEST_NAME}.txt "--play-fast" "test-regularizedcsp-test.xml")
|
||||
#
|
||||
# ADD_TEST(compare_${TEST_NAME} "$ENV{OV_BINARY_PATH}/test_accuracy.${EXT}" "${OS_FLAGS}" "${OV_LOGFILE}" ${COMPARE_THRESHOLD})
|
||||
#
|
||||
# # It would be better to clean last, but we can't do this as it will delete the
|
||||
# # output we wish to include, and we can't prevent clean from running if a prev. test fails
|
||||
# # We need the clean to be sure that the comparator stage is not getting data from a previous run.
|
||||
# SET_TESTS_PROPERTIES(run_${TEST_NAME}_Train PROPERTIES DEPENDS clean_${TEST_NAME})
|
||||
# SET_TESTS_PROPERTIES(run_${TEST_NAME}_Train PROPERTIES ATTACHED_FILES_ON_FAIL ${OV_LOGFILE})
|
||||
#
|
||||
# SET_TESTS_PROPERTIES(run_${TEST_NAME}_Test PROPERTIES DEPENDS run_${TEST_NAME}_Train)
|
||||
# SET_TESTS_PROPERTIES(run_${TEST_NAME}_Test PROPERTIES ATTACHED_FILES_ON_FAIL ${OV_LOGFILE})
|
||||
#
|
||||
# SET_TESTS_PROPERTIES(compare_${TEST_NAME} PROPERTIES DEPENDS run_${TEST_NAME}_Test})
|
||||
# SET_TESTS_PROPERTIES(compare_${TEST_NAME} PROPERTIES ATTACHED_FILES_ON_FAIL ${OV_LOGFILE})
|
||||
#
|
||||
# LIST(REMOVE_AT TEST_SHRINKS 0)
|
||||
# LIST(REMOVE_AT TEST_TIKHONOVS 0)
|
||||
# LIST(REMOVE_AT TEST_THRESHOLDS 0)
|
||||
#
|
||||
#ENDFOREACH(TEST_NAME)
|
||||
|
||||
|
||||
+746
@@ -0,0 +1,746 @@
|
||||
<OpenViBE-Scenario>
|
||||
<FormatVersion>1</FormatVersion>
|
||||
<Creator>openvibe</Creator>
|
||||
<CreatorVersion>2.0</CreatorVersion>
|
||||
<Boxes>
|
||||
<Box>
|
||||
<Identifier>(0x00000073, 0x00000fa8)</Identifier>
|
||||
<Name>Hilbert Transform</Name>
|
||||
<AlgorithmClassIdentifier>(0x7878a47f, 0x9a8fe349)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Input Signal</Name>
|
||||
</Input>
|
||||
</Inputs>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Hilbert Transform</Name>
|
||||
</Output>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Envelope</Name>
|
||||
</Output>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Phase</Name>
|
||||
</Output>
|
||||
</Outputs>
|
||||
<Attributes>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
|
||||
<Value>272</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x1fa963f5, 0x1a638cd4)</Identifier>
|
||||
<Value>38</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x207c9054, 0x3c841b63)</Identifier>
|
||||
<Value>304</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
|
||||
<Value>(0xceff4a87, 0xffc5ce08)</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xad100179, 0xa3c984ab)</Identifier>
|
||||
<Value>121</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
||||
<Value>3</Value>
|
||||
</Attribute>
|
||||
<Attribute>
|
||||
<Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
|
||||
<Value>1</Value>
|
||||
</Attribute>
|
||||
</Attributes>
|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x00000f8a, 0x00002000)</Identifier>
|
||||
<Name>CSV File Writer</Name>
|
||||
<AlgorithmClassIdentifier>(0x2c9312f1, 0x2d6613e5)</AlgorithmClassIdentifier>
|
||||
<Inputs>
|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Streamed matrix</Name>
|
||||
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|
||||
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|
||||
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|
||||
<Setting>
|
||||
<TypeIdentifier>(0x330306dd, 0x74a95f98)</TypeIdentifier>
|
||||
<Name>Filename</Name>
|
||||
<DefaultValue>record-[$core{date}-$core{time}].csv</DefaultValue>
|
||||
<Value>${Player_ScenarioDirectory}/phase_HilbertTransform.csv</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x79a9edeb, 0x245d83fc)</TypeIdentifier>
|
||||
<Name>Column separator</Name>
|
||||
<DefaultValue>;</DefaultValue>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
|
||||
<Name>Precision</Name>
|
||||
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|
||||
<Value>10</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<Name>CSV File Writer</Name>
|
||||
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|
||||
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|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Streamed matrix</Name>
|
||||
</Input>
|
||||
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|
||||
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|
||||
<Setting>
|
||||
<TypeIdentifier>(0x330306dd, 0x74a95f98)</TypeIdentifier>
|
||||
<Name>Filename</Name>
|
||||
<DefaultValue>record-[$core{date}-$core{time}].csv</DefaultValue>
|
||||
<Value>${Player_ScenarioDirectory}/envelope_HilbertTransform.csv</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x79a9edeb, 0x245d83fc)</TypeIdentifier>
|
||||
<Name>Column separator</Name>
|
||||
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|
||||
<Value>;</Value>
|
||||
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|
||||
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|
||||
<Setting>
|
||||
<TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
|
||||
<Name>Precision</Name>
|
||||
<DefaultValue>10</DefaultValue>
|
||||
<Value>10</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<Identifier>(0xad100179, 0xa3c984ab)</Identifier>
|
||||
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|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
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|
||||
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|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
</Box>
|
||||
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|
||||
<Identifier>(0x00003ea7, 0x0000084b)</Identifier>
|
||||
<Name>Channel Selector</Name>
|
||||
<AlgorithmClassIdentifier>(0x361722e8, 0x311574e8)</AlgorithmClassIdentifier>
|
||||
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|
||||
<Input>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Input signal</Name>
|
||||
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|
||||
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|
||||
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|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>Output signal</Name>
|
||||
</Output>
|
||||
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|
||||
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|
||||
<Setting>
|
||||
<TypeIdentifier>(0x79a9edeb, 0x245d83fc)</TypeIdentifier>
|
||||
<Name>Channel List</Name>
|
||||
<DefaultValue>:</DefaultValue>
|
||||
<Value>1</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x3bcf9e67, 0x0c23994d)</TypeIdentifier>
|
||||
<Name>Action</Name>
|
||||
<DefaultValue>Select</DefaultValue>
|
||||
<Value>Select</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x666f25e9, 0x3e5738d6)</TypeIdentifier>
|
||||
<Name>Channel Matching Method</Name>
|
||||
<DefaultValue>Smart</DefaultValue>
|
||||
<Value>Smart</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<Value>176</Value>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<Value>304</Value>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<Attribute>
|
||||
<Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
|
||||
<Value>false</Value>
|
||||
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|
||||
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|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<Value>1</Value>
|
||||
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|
||||
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|
||||
</Box>
|
||||
<Box>
|
||||
<Identifier>(0x0000468f, 0x00007d08)</Identifier>
|
||||
<Name>GDF file reader</Name>
|
||||
<AlgorithmClassIdentifier>(0x3eeb1264, 0x4edfbd9a)</AlgorithmClassIdentifier>
|
||||
<Outputs>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x403488e7, 0x565d70b6)</TypeIdentifier>
|
||||
<Name>Experiment information</Name>
|
||||
</Output>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
|
||||
<Name>EEG stream</Name>
|
||||
</Output>
|
||||
<Output>
|
||||
<TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
|
||||
<Name>Stimulations</Name>
|
||||
</Output>
|
||||
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|
||||
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|
||||
<Setting>
|
||||
<TypeIdentifier>(0x330306dd, 0x74a95f98)</TypeIdentifier>
|
||||
<Name>Filename</Name>
|
||||
<DefaultValue></DefaultValue>
|
||||
<Value>${Path_Data}/scenarios/signals/real-hand-movements.gdf</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
|
||||
<Name>Samples per buffer</Name>
|
||||
<DefaultValue>32</DefaultValue>
|
||||
<Value>32</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
|
||||
<Name>Subtract physical minimum</Name>
|
||||
<DefaultValue>False</DefaultValue>
|
||||
<Value>False</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<Box>
|
||||
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|
||||
<Name>CSV File Writer</Name>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<Name>Streamed matrix</Name>
|
||||
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|
||||
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|
||||
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|
||||
<Setting>
|
||||
<TypeIdentifier>(0x330306dd, 0x74a95f98)</TypeIdentifier>
|
||||
<Name>Filename</Name>
|
||||
<DefaultValue>record-[$core{date}-$core{time}].csv</DefaultValue>
|
||||
<Value>${Player_ScenarioDirectory}/hilbert_HilbertTransform.csv</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
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|
||||
<Setting>
|
||||
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|
||||
<Name>Column separator</Name>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<Setting>
|
||||
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|
||||
<Name>Precision</Name>
|
||||
<DefaultValue>10</DefaultValue>
|
||||
<Value>10</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<Name>Clock stimulator</Name>
|
||||
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|
||||
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|
||||
<Output>
|
||||
<TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
|
||||
<Name>Generated stimulations</Name>
|
||||
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|
||||
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|
||||
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|
||||
<Setting>
|
||||
<TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
|
||||
<Name>Interstimulation interval (in sec)</Name>
|
||||
<DefaultValue>1.0</DefaultValue>
|
||||
<Value>1.05</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
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|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2c132d6e, 0x44ab0d97)</TypeIdentifier>
|
||||
<Name>Stimulation</Name>
|
||||
<DefaultValue>OVTK_StimulationId_Label_00</DefaultValue>
|
||||
<Value>OVTK_StimulationId_Label_00</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
|
||||
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|
||||
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|
||||
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|
||||
<Identifier>(0xce18836a, 0x9c0eb403)</Identifier>
|
||||
<Value>2</Value>
|
||||
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|
||||
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|
||||
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|
||||
<Box>
|
||||
<Identifier>(0x00007e4d, 0x00004f1e)</Identifier>
|
||||
<Name>Player Controller</Name>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<Name>Stimulations</Name>
|
||||
</Input>
|
||||
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|
||||
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|
||||
<Setting>
|
||||
<TypeIdentifier>(0x2c132d6e, 0x44ab0d97)</TypeIdentifier>
|
||||
<Name>Stimulation name</Name>
|
||||
<DefaultValue>OVTK_StimulationId_Label_00</DefaultValue>
|
||||
<Value>OVTK_StimulationId_Label_00</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
<Setting>
|
||||
<TypeIdentifier>(0xcc14d8d6, 0xf27ecb73)</TypeIdentifier>
|
||||
<Name>Action to perform</Name>
|
||||
<DefaultValue>Pause</DefaultValue>
|
||||
<Value>Stop</Value>
|
||||
<Modifiability>false</Modifiability>
|
||||
</Setting>
|
||||
</Settings>
|
||||
<Attributes>
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+513
@@ -0,0 +1,513 @@
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||||
Time(s);Time signal;Sampling Rate
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0;652.872498;512
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0.396484;672.945435;
|
||||
0.398438;676.477295;
|
||||
0.400391;674.486816;
|
||||
0.402344;669.779358;
|
||||
0.404297;670.058228;
|
||||
0.40625;668.061401;
|
||||
0.408203;667.655762;
|
||||
0.410156;669.969543;
|
||||
0.412109;675.294861;
|
||||
0.414062;679.096069;
|
||||
0.416016;679.948181;
|
||||
0.417969;680.92511;
|
||||
0.419922;680.642456;
|
||||
0.421875;678.643799;
|
||||
0.423828;679.495728;
|
||||
0.425781;692.933594;
|
||||
0.427734;699.030334;
|
||||
0.429688;690.31427;
|
||||
0.431641;690.498047;
|
||||
0.433594;699.729614;
|
||||
0.435547;697.159485;
|
||||
0.4375;703.307434;
|
||||
0.439453;699.613892;
|
||||
0.441406;690.088806;
|
||||
0.443359;687.119934;
|
||||
0.445312;689.565552;
|
||||
0.447266;685.292297;
|
||||
0.449219;684.258606;
|
||||
0.451172;683.349365;
|
||||
0.453125;689.053528;
|
||||
0.455078;686.921387;
|
||||
0.457031;693.03595;
|
||||
0.458984;694.41925;
|
||||
0.460938;688.307007;
|
||||
0.462891;687.354919;
|
||||
0.464844;697.202942;
|
||||
0.466797;695.407776;
|
||||
0.46875;688.515198;
|
||||
0.470703;690.002502;
|
||||
0.472656;694.465637;
|
||||
0.474609;691.710022;
|
||||
0.476562;692.325073;
|
||||
0.478516;699.274048;
|
||||
0.480469;698.283752;
|
||||
0.482422;694.051758;
|
||||
0.484375;689.861389;
|
||||
0.486328;681.534729;
|
||||
0.488281;674.988953;
|
||||
0.490234;670.250732;
|
||||
0.492188;674.838623;
|
||||
0.494141;671.986084;
|
||||
0.496094;668.887207;
|
||||
0.498047;665.776855;
|
||||
0.5;669.816101;
|
||||
0.501953;665.739136;
|
||||
0.503906;659.799988;
|
||||
0.505859;658.712219;
|
||||
0.507812;656.40741;
|
||||
0.509766;663.074097;
|
||||
0.511719;679.350891;
|
||||
0.513672;688.225586;
|
||||
0.515625;687.647949;
|
||||
0.517578;686.26709;
|
||||
0.519531;669.194885;
|
||||
0.521484;654.585266;
|
||||
0.523438;649.088074;
|
||||
0.525391;655.951904;
|
||||
0.527344;661.500305;
|
||||
0.529297;668.91925;
|
||||
0.53125;670.090271;
|
||||
0.533203;665.431274;
|
||||
0.535156;657.512634;
|
||||
0.537109;663.687683;
|
||||
0.539062;673.667297;
|
||||
0.541016;674.910461;
|
||||
0.542969;674.081299;
|
||||
0.544922;683.475586;
|
||||
0.546875;678.487244;
|
||||
0.548828;673.630066;
|
||||
0.550781;681.811768;
|
||||
0.552734;689.033569;
|
||||
0.554688;686.033325;
|
||||
0.556641;689.177368;
|
||||
0.558594;697.444214;
|
||||
0.560547;699.110291;
|
||||
0.5625;693.176025;
|
||||
0.564453;693.561462;
|
||||
0.566406;686.230286;
|
||||
0.568359;682.510132;
|
||||
0.570312;675.486328;
|
||||
0.572266;686.663818;
|
||||
0.574219;684.883179;
|
||||
0.576172;680.887878;
|
||||
0.578125;687.027466;
|
||||
0.580078;688.646545;
|
||||
0.582031;678.338684;
|
||||
0.583984;678.789551;
|
||||
0.585938;681.331421;
|
||||
0.587891;678.272583;
|
||||
0.589844;678.695068;
|
||||
0.591797;681.442383;
|
||||
0.59375;688.903564;
|
||||
0.595703;682.258362;
|
||||
0.597656;679.974487;
|
||||
0.599609;676.200806;
|
||||
0.601562;671.150391;
|
||||
0.603516;669.976929;
|
||||
0.605469;672.728027;
|
||||
0.607422;667.427673;
|
||||
0.609375;659.651062;
|
||||
0.611328;663.85553;
|
||||
0.613281;668.456421;
|
||||
0.615234;667.265991;
|
||||
0.617188;671.815918;
|
||||
0.619141;674.233948;
|
||||
0.621094;678.567688;
|
||||
0.623047;675.043823;
|
||||
0.625;676.161865;
|
||||
0.626953;675.914001;
|
||||
0.628906;673.483826;
|
||||
0.630859;674.821716;
|
||||
0.632812;681.872803;
|
||||
0.634766;678.102356;
|
||||
0.636719;679.971741;
|
||||
0.638672;675.819397;
|
||||
0.640625;672.045898;
|
||||
0.642578;669.679443;
|
||||
0.644531;672.906067;
|
||||
0.646484;671.237793;
|
||||
0.648438;670.16571;
|
||||
0.650391;670.394592;
|
||||
0.652344;678.347229;
|
||||
0.654297;681.720337;
|
||||
0.65625;680.292969;
|
||||
0.658203;682.934631;
|
||||
0.660156;679.593079;
|
||||
0.662109;676.283142;
|
||||
0.664062;677.345581;
|
||||
0.666016;679.686707;
|
||||
0.667969;676.852173;
|
||||
0.669922;676.642395;
|
||||
0.671875;680.181885;
|
||||
0.673828;682.402588;
|
||||
0.675781;680.5802;
|
||||
0.677734;681.406555;
|
||||
0.679688;684.013855;
|
||||
0.681641;683.778564;
|
||||
0.683594;688.911316;
|
||||
0.685547;692.84491;
|
||||
0.6875;679.803955;
|
||||
0.689453;668.471191;
|
||||
0.691406;667.106995;
|
||||
0.693359;668.250977;
|
||||
0.695312;671.102417;
|
||||
0.697266;679.14801;
|
||||
0.699219;680.746338;
|
||||
0.701172;669.396301;
|
||||
0.703125;660.809753;
|
||||
0.705078;664.460632;
|
||||
0.707031;663.041809;
|
||||
0.708984;664.592041;
|
||||
0.710938;668.405273;
|
||||
0.712891;665.529236;
|
||||
0.714844;662.243225;
|
||||
0.716797;658.81781;
|
||||
0.71875;662.719116;
|
||||
0.720703;659.927185;
|
||||
0.722656;652.529053;
|
||||
0.724609;658.648926;
|
||||
0.726562;659.833252;
|
||||
0.728516;658.903503;
|
||||
0.730469;662.409668;
|
||||
0.732422;667.693542;
|
||||
0.734375;667.575745;
|
||||
0.736328;670.942627;
|
||||
0.738281;679.407349;
|
||||
0.740234;679.977356;
|
||||
0.742188;677.550415;
|
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0.744141;673.245544;
|
||||
0.746094;675.380981;
|
||||
0.748047;673.00238;
|
||||
0.75;675.703186;
|
||||
0.751953;671.788635;
|
||||
0.753906;665.445496;
|
||||
0.755859;667.666626;
|
||||
0.757812;677.88208;
|
||||
0.759766;682.865051;
|
||||
0.761719;678.304626;
|
||||
0.763672;674.659851;
|
||||
0.765625;672.701172;
|
||||
0.767578;682.170105;
|
||||
0.769531;679.32489;
|
||||
0.771484;678.649231;
|
||||
0.773438;681.970276;
|
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0.775391;679.308411;
|
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0.777344;679.949829;
|
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0.779297;677.177856;
|
||||
0.78125;664.491089;
|
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0.783203;664.837341;
|
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0.785156;672.92334;
|
||||
0.787109;676.017578;
|
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0.789062;673.882507;
|
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0.791016;675.458923;
|
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0.792969;681.763733;
|
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0.794922;678.458313;
|
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0.796875;674.974854;
|
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0.798828;673.329163;
|
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0.800781;670.157288;
|
||||
0.802734;668.537476;
|
||||
0.804688;671.010498;
|
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0.806641;666.601807;
|
||||
0.808594;661.410767;
|
||||
0.810547;665.279053;
|
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0.8125;666.392334;
|
||||
0.814453;662.331848;
|
||||
0.816406;653.432068;
|
||||
0.818359;655.363708;
|
||||
0.820312;658.257629;
|
||||
0.822266;648.640381;
|
||||
0.824219;640.110779;
|
||||
0.826172;631.641174;
|
||||
0.828125;620.967773;
|
||||
0.830078;615.977905;
|
||||
0.832031;612.672424;
|
||||
0.833984;609.831665;
|
||||
0.835938;601.372314;
|
||||
0.837891;595.485596;
|
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0.839844;589.810425;
|
||||
0.841797;585.12323;
|
||||
0.84375;579.608276;
|
||||
0.845703;582.709656;
|
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0.847656;580.177246;
|
||||
0.849609;575.39917;
|
||||
0.851562;574.131409;
|
||||
0.853516;575.011475;
|
||||
0.855469;570.915222;
|
||||
0.857422;568.846863;
|
||||
0.859375;564.63678;
|
||||
0.861328;556.245422;
|
||||
0.863281;554.104431;
|
||||
0.865234;563.10321;
|
||||
0.867188;563.672424;
|
||||
0.869141;561.671387;
|
||||
0.871094;568.506287;
|
||||
0.873047;575.075989;
|
||||
0.875;574.860413;
|
||||
0.876953;578.382874;
|
||||
0.878906;582.723694;
|
||||
0.880859;584.168152;
|
||||
0.882812;583.604431;
|
||||
0.884766;585.24054;
|
||||
0.886719;590.444824;
|
||||
0.888672;596.945923;
|
||||
0.890625;600.204895;
|
||||
0.892578;598.114441;
|
||||
0.894531;602.153442;
|
||||
0.896484;609.802002;
|
||||
0.898438;609.36322;
|
||||
0.900391;619.398682;
|
||||
0.902344;619.511292;
|
||||
0.904297;614.911499;
|
||||
0.90625;611.925903;
|
||||
0.908203;606.397217;
|
||||
0.910156;605.44281;
|
||||
0.912109;616.871704;
|
||||
0.914062;629.886719;
|
||||
0.916016;635.963623;
|
||||
0.917969;641.252197;
|
||||
0.919922;644.724121;
|
||||
0.921875;636.687134;
|
||||
0.923828;642.013428;
|
||||
0.925781;645.872314;
|
||||
0.927734;640.324097;
|
||||
0.929688;646.744385;
|
||||
0.931641;650.049866;
|
||||
0.933594;648.357483;
|
||||
0.935547;645.675232;
|
||||
0.9375;650.957153;
|
||||
0.939453;655.659302;
|
||||
0.941406;652.33313;
|
||||
0.943359;648.758362;
|
||||
0.945312;651.058838;
|
||||
0.947266;655.05365;
|
||||
0.949219;653.973022;
|
||||
0.951172;656.587891;
|
||||
0.953125;655.664368;
|
||||
0.955078;655.298584;
|
||||
0.957031;663.684753;
|
||||
0.958984;665.117493;
|
||||
0.960938;659.85498;
|
||||
0.962891;654.232422;
|
||||
0.964844;659.254333;
|
||||
0.966797;660.307983;
|
||||
0.96875;654.838379;
|
||||
0.970703;661.702332;
|
||||
0.972656;668.319153;
|
||||
0.974609;667.06842;
|
||||
0.976562;669.422668;
|
||||
0.978516;669.911194;
|
||||
0.980469;664.245239;
|
||||
0.982422;655.385925;
|
||||
0.984375;659.391052;
|
||||
0.986328;663.098572;
|
||||
0.988281;651.90271;
|
||||
0.990234;647.950806;
|
||||
0.992188;657.725586;
|
||||
0.994141;661.506836;
|
||||
0.996094;665.620667;
|
||||
0.998047;672.148987;
|
||||
|
+513
@@ -0,0 +1,513 @@
|
||||
Time(s);Time signal;Sampling Rate
|
||||
0;28.981428;512
|
||||
0.001953;42.533489;
|
||||
0.003906;47.134895;
|
||||
0.005859;44.134022;
|
||||
0.007812;38.354893;
|
||||
0.009766;47.397827;
|
||||
0.011719;48.880821;
|
||||
0.013672;54.954945;
|
||||
0.015625;60.355793;
|
||||
0.017578;62.928974;
|
||||
0.019531;55.618874;
|
||||
0.021484;48.280991;
|
||||
0.023438;46.291126;
|
||||
0.025391;42.262794;
|
||||
0.027344;38.17527;
|
||||
0.029297;40.746456;
|
||||
0.03125;41.668076;
|
||||
0.033203;41.406368;
|
||||
0.035156;39.970119;
|
||||
0.037109;46.752495;
|
||||
0.039062;44.760719;
|
||||
0.041016;36.394112;
|
||||
0.042969;41.215172;
|
||||
0.044922;47.307755;
|
||||
0.046875;38.753983;
|
||||
0.048828;36.225052;
|
||||
0.050781;38.588387;
|
||||
0.052734;45.200058;
|
||||
0.054688;44.333199;
|
||||
0.056641;45.128956;
|
||||
0.058594;49.382904;
|
||||
0.060547;48.119011;
|
||||
0.0625;41.630928;
|
||||
0.064453;39.523262;
|
||||
0.066406;33.259003;
|
||||
0.068359;33.013283;
|
||||
0.070312;39.875595;
|
||||
0.072266;38.035378;
|
||||
0.074219;36.587803;
|
||||
0.076172;37.818752;
|
||||
0.078125;31.631701;
|
||||
0.080078;30.422977;
|
||||
0.082031;38.480396;
|
||||
0.083984;40.629364;
|
||||
0.085938;34.120766;
|
||||
0.087891;36.048504;
|
||||
0.089844;39.391136;
|
||||
0.091797;42.849953;
|
||||
0.09375;44.074574;
|
||||
0.095703;49.756237;
|
||||
0.097656;46.069683;
|
||||
0.099609;40.308197;
|
||||
0.101562;39.125095;
|
||||
0.103516;44.027828;
|
||||
0.105469;41.049587;
|
||||
0.107422;38.883476;
|
||||
0.109375;39.536407;
|
||||
0.111328;41.074429;
|
||||
0.113281;36.20052;
|
||||
0.115234;35.350887;
|
||||
0.117188;35.939304;
|
||||
0.119141;29.167889;
|
||||
0.121094;16.342152;
|
||||
0.123047;15.611171;
|
||||
0.125;16.522947;
|
||||
0.126953;17.34523;
|
||||
0.128906;20.528202;
|
||||
0.130859;25.429874;
|
||||
0.132812;25.915106;
|
||||
0.134766;26.125984;
|
||||
0.136719;28.386164;
|
||||
0.138672;28.47311;
|
||||
0.140625;26.497925;
|
||||
0.142578;16.317856;
|
||||
0.144531;21.346485;
|
||||
0.146484;24.65727;
|
||||
0.148438;27.646727;
|
||||
0.150391;32.761471;
|
||||
0.152344;35.805737;
|
||||
0.154297;32.539322;
|
||||
0.15625;33.611752;
|
||||
0.158203;31.097401;
|
||||
0.160156;24.689838;
|
||||
0.162109;20.777893;
|
||||
0.164062;20.389;
|
||||
0.166016;13.356441;
|
||||
0.167969;8.95355;
|
||||
0.169922;11.394264;
|
||||
0.171875;10.890619;
|
||||
0.173828;3.556692;
|
||||
0.175781;6.071507;
|
||||
0.177734;5.692522;
|
||||
0.179688;6.904676;
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0.949219;47.730755;
|
||||
0.951172;48.471378;
|
||||
0.953125;45.506866;
|
||||
0.955078;50.238186;
|
||||
0.957031;50.173634;
|
||||
0.958984;42.120224;
|
||||
0.960938;37.473175;
|
||||
0.962891;41.070911;
|
||||
0.964844;45.978966;
|
||||
0.966797;40.326958;
|
||||
0.96875;43.826504;
|
||||
0.970703;50.288212;
|
||||
0.972656;45.363804;
|
||||
0.974609;41.452553;
|
||||
0.976562;40.376003;
|
||||
0.978516;33.762836;
|
||||
0.980469;28.353556;
|
||||
0.982422;30.967506;
|
||||
0.984375;39.286591;
|
||||
0.986328;31.373966;
|
||||
0.988281;29.597958;
|
||||
0.990234;40.77985;
|
||||
0.992188;48.465214;
|
||||
0.994141;43.518177;
|
||||
0.996094;47.480377;
|
||||
0.998047;32.929554;
|
||||
|
+513
@@ -0,0 +1,513 @@
|
||||
Time(s);Time signal;Sampling Rate
|
||||
0;3.097188;512
|
||||
0.001953;3.07633;
|
||||
0.003906;3.069826;
|
||||
0.005859;3.075173;
|
||||
0.007812;3.083173;
|
||||
0.009766;3.069207;
|
||||
0.011719;3.067177;
|
||||
0.013672;3.058007;
|
||||
0.015625;3.050604;
|
||||
0.017578;3.048189;
|
||||
0.019531;3.060055;
|
||||
0.021484;3.070759;
|
||||
0.023438;3.07357;
|
||||
0.025391;3.079629;
|
||||
0.027344;3.085208;
|
||||
0.029297;3.081203;
|
||||
0.03125;3.079854;
|
||||
0.033203;3.080383;
|
||||
0.035156;3.082213;
|
||||
0.037109;3.072226;
|
||||
0.039062;3.07599;
|
||||
0.041016;3.088025;
|
||||
0.042969;3.080251;
|
||||
0.044922;3.072052;
|
||||
0.046875;3.084892;
|
||||
0.048828;3.088162;
|
||||
0.050781;3.084298;
|
||||
0.052734;3.074636;
|
||||
0.054688;3.076343;
|
||||
0.056641;3.07505;
|
||||
0.058594;3.069163;
|
||||
0.060547;3.071798;
|
||||
0.0625;3.081346;
|
||||
0.064453;3.084389;
|
||||
0.066406;3.09341;
|
||||
0.068359;3.093196;
|
||||
0.070312;3.083218;
|
||||
0.072266;3.086287;
|
||||
0.074219;3.088233;
|
||||
0.076172;3.08667;
|
||||
0.078125;3.095679;
|
||||
0.080078;3.096942;
|
||||
0.082031;3.084925;
|
||||
0.083984;3.082463;
|
||||
0.085938;3.091877;
|
||||
0.087891;3.088733;
|
||||
0.089844;3.083781;
|
||||
0.091797;3.078914;
|
||||
0.09375;3.077195;
|
||||
0.095703;3.069401;
|
||||
0.097656;3.075539;
|
||||
0.099609;3.083792;
|
||||
0.101562;3.085197;
|
||||
0.103516;3.078291;
|
||||
0.105469;3.083019;
|
||||
0.107422;3.086012;
|
||||
0.109375;3.085115;
|
||||
0.111328;3.08322;
|
||||
0.113281;3.090414;
|
||||
0.115234;3.091482;
|
||||
0.117188;3.091007;
|
||||
0.119141;3.100885;
|
||||
0.121094;3.118671;
|
||||
0.123047;3.119367;
|
||||
0.125;3.118003;
|
||||
0.126953;3.116641;
|
||||
0.128906;3.11198;
|
||||
0.130859;3.10487;
|
||||
0.132812;3.104407;
|
||||
0.134766;3.103989;
|
||||
0.136719;3.10094;
|
||||
0.138672;3.100865;
|
||||
0.140625;3.10407;
|
||||
0.142578;3.118315;
|
||||
0.144531;3.110813;
|
||||
0.146484;3.10608;
|
||||
0.148438;3.101737;
|
||||
0.150391;3.094471;
|
||||
0.152344;3.090622;
|
||||
0.154297;3.095517;
|
||||
0.15625;3.094173;
|
||||
0.158203;3.098109;
|
||||
0.160156;3.107191;
|
||||
0.162109;3.112552;
|
||||
0.164062;3.113174;
|
||||
0.166016;3.123014;
|
||||
0.167969;3.129043;
|
||||
0.169922;3.125542;
|
||||
0.171875;3.126344;
|
||||
0.173828;3.136588;
|
||||
0.175781;3.132988;
|
||||
0.177734;3.133522;
|
||||
0.179688;3.131767;
|
||||
0.181641;3.13068;
|
||||
0.183594;3.127816;
|
||||
0.185547;3.128898;
|
||||
0.1875;3.125444;
|
||||
0.189453;3.117634;
|
||||
0.191406;3.119542;
|
||||
0.193359;3.136653;
|
||||
0.195312;3.125466;
|
||||
0.197266;3.113787;
|
||||
0.199219;3.124379;
|
||||
0.201172;3.141151;
|
||||
0.203125;-3.139929;
|
||||
0.205078;-3.135257;
|
||||
0.207031;-3.126303;
|
||||
0.208984;-3.135338;
|
||||
0.210938;3.141327;
|
||||
0.212891;-3.140974;
|
||||
0.214844;-3.138109;
|
||||
0.216797;-3.125593;
|
||||
0.21875;-3.115173;
|
||||
0.220703;-3.118337;
|
||||
0.222656;-3.122142;
|
||||
0.224609;-3.128323;
|
||||
0.226562;-3.120599;
|
||||
0.228516;-3.126431;
|
||||
0.230469;-3.133207;
|
||||
0.232422;-3.139365;
|
||||
0.234375;-3.139642;
|
||||
0.236328;3.134071;
|
||||
0.238281;3.129321;
|
||||
0.240234;3.129448;
|
||||
0.242188;3.129903;
|
||||
0.244141;3.123433;
|
||||
0.246094;3.129284;
|
||||
0.248047;3.134467;
|
||||
0.25;3.129251;
|
||||
0.251953;3.131762;
|
||||
0.253906;3.134993;
|
||||
0.255859;3.136174;
|
||||
0.257812;3.137727;
|
||||
0.259766;-3.141566;
|
||||
0.261719;-3.140273;
|
||||
0.263672;3.131638;
|
||||
0.265625;3.139344;
|
||||
0.267578;-3.140288;
|
||||
0.269531;3.134024;
|
||||
0.271484;3.131357;
|
||||
0.273438;3.139041;
|
||||
0.275391;-3.136429;
|
||||
0.277344;-3.141036;
|
||||
0.279297;3.138864;
|
||||
0.28125;-3.133502;
|
||||
0.283203;-3.133714;
|
||||
0.285156;-3.132497;
|
||||
0.287109;-3.134517;
|
||||
0.289062;3.135531;
|
||||
0.291016;3.125844;
|
||||
0.292969;3.131409;
|
||||
0.294922;3.128625;
|
||||
0.296875;3.121791;
|
||||
0.298828;3.128163;
|
||||
0.300781;-3.125436;
|
||||
0.302734;-3.119581;
|
||||
0.304688;-3.128702;
|
||||
0.306641;-3.123122;
|
||||
0.308594;-3.12035;
|
||||
0.310547;-3.123028;
|
||||
0.3125;-3.126873;
|
||||
0.314453;-3.11689;
|
||||
0.316406;-3.11473;
|
||||
0.318359;-3.113946;
|
||||
0.320312;-3.1135;
|
||||
0.322266;-3.126894;
|
||||
0.324219;-3.124253;
|
||||
0.326172;-3.099831;
|
||||
0.328125;-3.085108;
|
||||
0.330078;-3.069119;
|
||||
0.332031;-3.069197;
|
||||
0.333984;-3.073479;
|
||||
0.335938;-3.095567;
|
||||
0.337891;-3.109336;
|
||||
0.339844;-3.095849;
|
||||
0.341797;-3.081834;
|
||||
0.34375;-3.075554;
|
||||
0.345703;-3.071383;
|
||||
0.347656;-3.07533;
|
||||
0.349609;-3.084876;
|
||||
0.351562;-3.086492;
|
||||
0.353516;-3.081575;
|
||||
0.355469;-3.089163;
|
||||
0.357422;-3.090658;
|
||||
0.359375;-3.083164;
|
||||
0.361328;-3.086156;
|
||||
0.363281;-3.091323;
|
||||
0.365234;-3.091469;
|
||||
0.367188;-3.094861;
|
||||
0.369141;-3.104506;
|
||||
0.371094;-3.114676;
|
||||
0.373047;-3.111515;
|
||||
0.375;-3.116299;
|
||||
0.376953;-3.122826;
|
||||
0.378906;-3.124658;
|
||||
0.380859;-3.131804;
|
||||
0.382812;-3.140703;
|
||||
0.384766;3.137651;
|
||||
0.386719;3.139747;
|
||||
0.388672;3.139437;
|
||||
0.390625;3.138057;
|
||||
0.392578;3.140452;
|
||||
0.394531;3.136339;
|
||||
0.396484;3.13149;
|
||||
0.398438;3.140348;
|
||||
0.400391;-3.137099;
|
||||
0.402344;-3.137211;
|
||||
0.404297;3.141315;
|
||||
0.40625;3.141468;
|
||||
0.408203;3.133417;
|
||||
0.410156;3.128909;
|
||||
0.412109;3.124238;
|
||||
0.414062;3.129239;
|
||||
0.416016;3.129699;
|
||||
0.417969;3.13278;
|
||||
0.419922;3.132332;
|
||||
0.421875;3.132606;
|
||||
0.423828;3.119472;
|
||||
0.425781;3.117785;
|
||||
0.427734;3.134565;
|
||||
0.429688;-3.139252;
|
||||
0.431641;3.130426;
|
||||
0.433594;3.138496;
|
||||
0.435547;-3.140269;
|
||||
0.4375;-3.135042;
|
||||
0.439453;-3.119358;
|
||||
0.441406;-3.117261;
|
||||
0.443359;-3.125588;
|
||||
0.445312;-3.12381;
|
||||
0.447266;-3.123292;
|
||||
0.449219;-3.126344;
|
||||
0.451172;-3.132842;
|
||||
0.453125;-3.133108;
|
||||
0.455078;-3.131894;
|
||||
0.457031;-3.13537;
|
||||
0.458984;-3.123554;
|
||||
0.460938;-3.121847;
|
||||
0.462891;-3.131093;
|
||||
0.464844;-3.130348;
|
||||
0.466797;-3.115002;
|
||||
0.46875;-3.11751;
|
||||
0.470703;-3.123094;
|
||||
0.472656;-3.120169;
|
||||
0.474609;-3.114358;
|
||||
0.476562;-3.120203;
|
||||
0.478516;-3.114278;
|
||||
0.480469;-3.101744;
|
||||
0.482422;-3.095289;
|
||||
0.484375;-3.088413;
|
||||
0.486328;-3.085702;
|
||||
0.488281;-3.0889;
|
||||
0.490234;-3.098566;
|
||||
0.492188;-3.103051;
|
||||
0.494141;-3.097803;
|
||||
0.496094;-3.101387;
|
||||
0.498047;-3.105835;
|
||||
0.5;-3.110284;
|
||||
0.501953;-3.101672;
|
||||
0.503906;-3.111609;
|
||||
0.505859;-3.116504;
|
||||
0.507812;-3.130284;
|
||||
0.509766;3.137747;
|
||||
0.511719;3.133679;
|
||||
0.513672;-3.12922;
|
||||
0.515625;-3.118203;
|
||||
0.517578;-3.097677;
|
||||
0.519531;-3.087169;
|
||||
0.521484;-3.097208;
|
||||
0.523438;-3.120841;
|
||||
0.525391;-3.134931;
|
||||
0.527344;-3.140921;
|
||||
0.529297;-3.139118;
|
||||
0.53125;-3.131831;
|
||||
0.533203;-3.126093;
|
||||
0.535156;-3.13726;
|
||||
0.537109;3.129578;
|
||||
0.539062;3.130279;
|
||||
0.541016;3.138821;
|
||||
0.542969;3.132302;
|
||||
0.544922;3.136317;
|
||||
0.546875;-3.135306;
|
||||
0.548828;3.137387;
|
||||
0.550781;3.128603;
|
||||
0.552734;3.137864;
|
||||
0.554688;-3.141163;
|
||||
0.556641;3.137386;
|
||||
0.558594;-3.141376;
|
||||
0.560547;-3.124912;
|
||||
0.5625;-3.120549;
|
||||
0.564453;-3.114538;
|
||||
0.566406;-3.110133;
|
||||
0.568359;-3.110598;
|
||||
0.570312;-3.120473;
|
||||
0.572266;-3.128386;
|
||||
0.574219;-3.11319;
|
||||
0.576172;-3.121897;
|
||||
0.578125;-3.121531;
|
||||
0.580078;-3.110579;
|
||||
0.582031;-3.105755;
|
||||
0.583984;-3.119009;
|
||||
0.585938;-3.112398;
|
||||
0.587891;-3.115733;
|
||||
0.589844;-3.11605;
|
||||
0.591797;-3.123139;
|
||||
0.59375;-3.111946;
|
||||
0.595703;-3.103148;
|
||||
0.597656;-3.103153;
|
||||
0.599609;-3.100181;
|
||||
0.601562;-3.102168;
|
||||
0.603516;-3.109428;
|
||||
0.605469;-3.10765;
|
||||
0.607422;-3.102602;
|
||||
0.609375;-3.111921;
|
||||
0.611328;-3.126742;
|
||||
0.613281;-3.123473;
|
||||
0.615234;-3.128138;
|
||||
0.617188;-3.129877;
|
||||
0.619141;-3.1308;
|
||||
0.621094;-3.124955;
|
||||
0.623047;-3.121517;
|
||||
0.625;-3.123328;
|
||||
0.626953;-3.120505;
|
||||
0.628906;-3.120843;
|
||||
0.630859;-3.128668;
|
||||
0.632812;-3.122949;
|
||||
0.634766;-3.117347;
|
||||
0.636719;-3.116485;
|
||||
0.638672;-3.109823;
|
||||
0.640625;-3.112933;
|
||||
0.642578;-3.117654;
|
||||
0.644531;-3.121368;
|
||||
0.646484;-3.118372;
|
||||
0.648438;-3.123679;
|
||||
0.650391;-3.129243;
|
||||
0.652344;-3.134692;
|
||||
0.654297;-3.123738;
|
||||
0.65625;-3.123808;
|
||||
0.658203;-3.118511;
|
||||
0.660156;-3.113745;
|
||||
0.662109;-3.115279;
|
||||
0.664062;-3.120161;
|
||||
0.666016;-3.116469;
|
||||
0.667969;-3.115379;
|
||||
0.669922;-3.119467;
|
||||
0.671875;-3.121366;
|
||||
0.673828;-3.116179;
|
||||
0.675781;-3.114367;
|
||||
0.677734;-3.116018;
|
||||
0.679688;-3.11388;
|
||||
0.681641;-3.111349;
|
||||
0.683594;-3.111567;
|
||||
0.685547;-3.092909;
|
||||
0.6875;-3.079025;
|
||||
0.689453;-3.087428;
|
||||
0.691406;-3.098665;
|
||||
0.693359;-3.103894;
|
||||
0.695312;-3.110186;
|
||||
0.697266;-3.107746;
|
||||
0.699219;-3.091724;
|
||||
0.701172;-3.081896;
|
||||
0.703125;-3.094395;
|
||||
0.705078;-3.101435;
|
||||
0.707031;-3.101114;
|
||||
0.708984;-3.107702;
|
||||
0.710938;-3.103434;
|
||||
0.712891;-3.098916;
|
||||
0.714844;-3.099007;
|
||||
0.716797;-3.104402;
|
||||
0.71875;-3.108484;
|
||||
0.720703;-3.100152;
|
||||
0.722656;-3.111268;
|
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|
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0.966797;3.080482;
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|
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|
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|
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|
||||
0.980469;3.098894;
|
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0.982422;3.094324;
|
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0.984375;3.081977;
|
||||
0.986328;3.094261;
|
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0.988281;3.096175;
|
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|
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0.998047;3.092582;
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|
+1438
File diff suppressed because it is too large
Load Diff
+823
@@ -0,0 +1,823 @@
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||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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|
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||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
<Link>
|
||||
<Identifier>(0x00004a2f, 0x000027c8)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x000015f4, 0x00003233)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
||||
</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x00001d79, 0x0000094b)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
||||
</Target>
|
||||
</Link>
|
||||
<Link>
|
||||
<Identifier>(0x000051a5, 0x00006b77)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x00006bb1, 0x00002e53)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
||||
</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x000015f4, 0x00003233)</BoxIdentifier>
|
||||
<BoxInputIndex>2</BoxInputIndex>
|
||||
</Target>
|
||||
</Link>
|
||||
<Link>
|
||||
<Identifier>(0x00005af9, 0x000060fc)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x000001bf, 0x0000774e)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
||||
</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x00004f85, 0x000075c2)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
||||
</Target>
|
||||
</Link>
|
||||
<Link>
|
||||
<Identifier>(0x00006209, 0x000016ec)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x0000099b, 0x0000516a)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
||||
</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x000001bf, 0x0000774e)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
||||
</Target>
|
||||
</Link>
|
||||
<Link>
|
||||
<Identifier>(0x00006c27, 0x00001ea0)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x00005cd1, 0x00002308)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
||||
</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x000015f4, 0x00003233)</BoxIdentifier>
|
||||
<BoxInputIndex>0</BoxInputIndex>
|
||||
</Target>
|
||||
</Link>
|
||||
<Link>
|
||||
<Identifier>(0x0000777a, 0x00004134)</Identifier>
|
||||
<Source>
|
||||
<BoxIdentifier>(0x00005dde, 0x000059bc)</BoxIdentifier>
|
||||
<BoxOutputIndex>0</BoxOutputIndex>
|
||||
</Source>
|
||||
<Target>
|
||||
<BoxIdentifier>(0x000015f4, 0x00003233)</BoxIdentifier>
|
||||
<BoxInputIndex>1</BoxInputIndex>
|
||||
</Target>
|
||||
</Link>
|
||||
</Links>
|
||||
<Comments></Comments>
|
||||
<Metadata>
|
||||
<Entry>
|
||||
<Identifier>(0x0000775c, 0x000078ff)</Identifier>
|
||||
<Type>(0x3bcce5d2, 0x43f2d968)</Type>
|
||||
<Data>[{"boxIdentifier":"(0xffffffff, 0xffffffff)","childCount":1,"height":320,"identifier":"(0x00004b31, 0x000075b1)","name":"Default window","parentIdentifier":"(0xffffffff, 0xffffffff)","type":1,"width":480},{"boxIdentifier":"(0xffffffff, 0xffffffff)","childCount":1,"identifier":"(0x00007e8e, 0x00001b92)","index":0,"name":"Default tab","parentIdentifier":"(0x00004b31, 0x000075b1)","type":2},{"boxIdentifier":"(0xffffffff, 0xffffffff)","childCount":0,"identifier":"(0x00006ba8, 0x00005c33)","index":0,"name":"Empty","parentIdentifier":"(0x00007e8e, 0x00001b92)","type":0}]</Data>
|
||||
</Entry>
|
||||
</Metadata>
|
||||
</OpenViBE-Scenario>
|
||||
BIN
Binary file not shown.
|
After Width: | Height: | Size: 4.3 KiB |
BIN
Binary file not shown.
|
After Width: | Height: | Size: 47 KiB |
BIN
Binary file not shown.
|
After Width: | Height: | Size: 398 B |
+93
@@ -0,0 +1,93 @@
|
||||
|
||||
<html>
|
||||
<head>
|
||||
<style type="text/css">
|
||||
.sikuli-code {
|
||||
font-size: 20px;
|
||||
font-family: "Osaka-mono", Monospace;
|
||||
line-height: 1.5em;
|
||||
display:table-cell;
|
||||
white-space: pre-wrap; /* css-3 */
|
||||
white-space: -moz-pre-wrap !important; /* Mozilla, since 1999 */
|
||||
white-space: -pre-wrap; /* Opera 4-6 */
|
||||
white-space: -o-pre-wrap; /* Opera 7 */
|
||||
word-wrap: break-word; /* Internet Explorer 5.5+ */
|
||||
width: 99%; /* remove horizontal scroll-bar when viewing in IE7 */
|
||||
}
|
||||
.sikuli-code img {
|
||||
vertical-align: middle;
|
||||
margin: 2px;
|
||||
border: 1px solid #ccc;
|
||||
padding: 2px;
|
||||
-moz-border-radius: 5px;
|
||||
-webkit-border-radius: 5px;
|
||||
-moz-box-shadow: 1px 1px 1px gray;
|
||||
-webkit-box-shadow: 1px 1px 2px gray;
|
||||
}
|
||||
.kw {
|
||||
color: blue;
|
||||
}
|
||||
.skw {
|
||||
color: rgb(63, 127, 127);
|
||||
}
|
||||
|
||||
.str {
|
||||
color: rgb(128, 0, 0);
|
||||
}
|
||||
|
||||
.dig {
|
||||
color: rgb(128, 64, 0);
|
||||
}
|
||||
|
||||
.cmt {
|
||||
color: rgb(200, 0, 200);
|
||||
}
|
||||
|
||||
h2 {
|
||||
display: inline;
|
||||
font-weight: normal;
|
||||
}
|
||||
|
||||
.info {
|
||||
border-bottom: 1px solid #ddd;
|
||||
padding-bottom: 5px;
|
||||
margin-bottom: 20px;
|
||||
display: none;
|
||||
}
|
||||
|
||||
a {
|
||||
color: #9D2900;
|
||||
}
|
||||
|
||||
body {
|
||||
font-family: "Trebuchet MS", Arial, Sans-Serif;
|
||||
}
|
||||
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="info">
|
||||
<h2>testCrop.sikuli</h2> <a href="testCrop.zip">(Download this script)</a>
|
||||
</div>
|
||||
<pre class="sikuli-code">
|
||||
<span class="kw">def</span> setUp(self):
|
||||
<span class="kw">import</span> os
|
||||
ov_binany_path=os.environ[<span class="str">'OV_BINARY_PATH'</span>]
|
||||
self.terminal = App.open(<span class="str">"xterm -e "</span> +
|
||||
ov_binany_path +<span class="str">"/openvibe-designer.sh --no-session-management --open "</span> +
|
||||
ov_binany_path +<span class="str">"/share/openvibe/scenarios/box-tutorials/crop.xml"</span>)
|
||||
<span class="kw">while</span> <span class="kw">not</span> self.terminal.window():
|
||||
<span class="skw">wait</span>(<span class="dig">1</span>)
|
||||
<span class="skw">wait</span>(<img src="play.png" />,<span class="dig">10</span>)
|
||||
|
||||
<span class="kw">def</span> testRunCrop(self):
|
||||
<span class="skw">click</span>(<img src="play.png" />)
|
||||
<span class="skw">wait</span>(<img src="timesignal.png" />,<span class="dig">10</span>)
|
||||
<span class="kw">def</span> tearDown(self):
|
||||
mouseMove(Location(<span class="dig">0</span>,<span class="dig">0</span>))
|
||||
<span class="kw">if</span> self.terminal.window():
|
||||
App.close(self.terminal)
|
||||
self.terminal= None
|
||||
</pre>
|
||||
</body>
|
||||
</html>
|
||||
+19
@@ -0,0 +1,19 @@
|
||||
def setUp(self):
|
||||
import os
|
||||
ov_binany_path=os.environ['OV_BINARY_PATH']
|
||||
self.terminal = App.open("xterm -e " +
|
||||
ov_binany_path +"/openvibe-designer.sh --no-session-management --open " +
|
||||
ov_binany_path +"/share/openvibe/scenarios/box-tutorials/crop.xml")
|
||||
while not self.terminal.window():
|
||||
wait(1)
|
||||
wait("play.png",10)
|
||||
|
||||
def testRunCrop(self):
|
||||
click("play.png")
|
||||
wait("timesignal.png",10)
|
||||
def tearDown(self):
|
||||
mouseMove(Location(0,0))
|
||||
if self.terminal.window():
|
||||
App.close(self.terminal)
|
||||
self.terminal= None
|
||||
|
||||
+89
@@ -0,0 +1,89 @@
|
||||
|
||||
<html>
|
||||
<head>
|
||||
<style type="text/css">
|
||||
.sikuli-code {
|
||||
font-size: 20px;
|
||||
font-family: "Osaka-mono", Monospace;
|
||||
line-height: 1.5em;
|
||||
display:table-cell;
|
||||
white-space: pre-wrap; /* css-3 */
|
||||
white-space: -moz-pre-wrap !important; /* Mozilla, since 1999 */
|
||||
white-space: -pre-wrap; /* Opera 4-6 */
|
||||
white-space: -o-pre-wrap; /* Opera 7 */
|
||||
word-wrap: break-word; /* Internet Explorer 5.5+ */
|
||||
width: 99%; /* remove horizontal scroll-bar when viewing in IE7 */
|
||||
}
|
||||
.sikuli-code img {
|
||||
vertical-align: middle;
|
||||
margin: 2px;
|
||||
border: 1px solid #ccc;
|
||||
padding: 2px;
|
||||
-moz-border-radius: 5px;
|
||||
-webkit-border-radius: 5px;
|
||||
-moz-box-shadow: 1px 1px 1px gray;
|
||||
-webkit-box-shadow: 1px 1px 2px gray;
|
||||
}
|
||||
.kw {
|
||||
color: blue;
|
||||
}
|
||||
.skw {
|
||||
color: rgb(63, 127, 127);
|
||||
}
|
||||
|
||||
.str {
|
||||
color: rgb(128, 0, 0);
|
||||
}
|
||||
|
||||
.dig {
|
||||
color: rgb(128, 64, 0);
|
||||
}
|
||||
|
||||
.cmt {
|
||||
color: rgb(200, 0, 200);
|
||||
}
|
||||
|
||||
h2 {
|
||||
display: inline;
|
||||
font-weight: normal;
|
||||
}
|
||||
|
||||
.info {
|
||||
border-bottom: 1px solid #ddd;
|
||||
padding-bottom: 5px;
|
||||
margin-bottom: 20px;
|
||||
display: none;
|
||||
}
|
||||
|
||||
a {
|
||||
color: #9D2900;
|
||||
}
|
||||
|
||||
body {
|
||||
font-family: "Trebuchet MS", Arial, Sans-Serif;
|
||||
}
|
||||
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="info">
|
||||
<h2>testCrop.sikuli</h2> <a href="testCrop.zip">(Download this script)</a>
|
||||
</div>
|
||||
<pre class="sikuli-code">
|
||||
<span class="kw">def</span> setUp(self):
|
||||
<span class="kw">import</span> os
|
||||
ov_binany_path=os.environ[<span class="str">'OV_BINARY_PATH'</span>]
|
||||
self.terminal = App.open(<span class="str">"xterm -e "</span> +
|
||||
ov_binany_path +<span class="str">"/openvibe-designer.sh --no-session-management --play "</span> +
|
||||
ov_binany_path +<span class="str">"/share/openvibe/scenarios/box-tutorials/crop.xml"</span>)
|
||||
<span class="kw">while</span> <span class="kw">not</span> self.terminal.window():
|
||||
<span class="skw">wait</span>(<span class="dig">1</span>)
|
||||
<span class="kw">def</span> testRunOgreVisual(self):
|
||||
<span class="kw">assert</span>(exists(<img src="timesignal.png" />))
|
||||
<span class="kw">def</span> tearDown(self):
|
||||
<span class="kw">if</span> self.terminal.window():
|
||||
App.close(self.terminal)
|
||||
self.terminal= None
|
||||
</pre>
|
||||
</body>
|
||||
</html>
|
||||
+15
@@ -0,0 +1,15 @@
|
||||
def setUp(self):
|
||||
import os
|
||||
ov_binany_path=os.environ['OV_BINARY_PATH']
|
||||
self.terminal = App.open("xterm -e " +
|
||||
ov_binany_path +"/openvibe-designer.sh --no-session-management --play " +
|
||||
ov_binany_path +"/share/openvibe/scenarios/box-tutorials/crop.xml")
|
||||
while not self.terminal.window():
|
||||
wait(1)
|
||||
def testRunOgreVisual(self):
|
||||
assert(exists("timesignal.png"))
|
||||
def tearDown(self):
|
||||
if self.terminal.window():
|
||||
App.close(self.terminal)
|
||||
self.terminal= None
|
||||
|
||||
+89
@@ -0,0 +1,89 @@
|
||||
|
||||
<html>
|
||||
<head>
|
||||
<style type="text/css">
|
||||
.sikuli-code {
|
||||
font-size: 20px;
|
||||
font-family: "Osaka-mono", Monospace;
|
||||
line-height: 1.5em;
|
||||
display:table-cell;
|
||||
white-space: pre-wrap; /* css-3 */
|
||||
white-space: -moz-pre-wrap !important; /* Mozilla, since 1999 */
|
||||
white-space: -pre-wrap; /* Opera 4-6 */
|
||||
white-space: -o-pre-wrap; /* Opera 7 */
|
||||
word-wrap: break-word; /* Internet Explorer 5.5+ */
|
||||
width: 99%; /* remove horizontal scroll-bar when viewing in IE7 */
|
||||
}
|
||||
.sikuli-code img {
|
||||
vertical-align: middle;
|
||||
margin: 2px;
|
||||
border: 1px solid #ccc;
|
||||
padding: 2px;
|
||||
-moz-border-radius: 5px;
|
||||
-webkit-border-radius: 5px;
|
||||
-moz-box-shadow: 1px 1px 1px gray;
|
||||
-webkit-box-shadow: 1px 1px 2px gray;
|
||||
}
|
||||
.kw {
|
||||
color: blue;
|
||||
}
|
||||
.skw {
|
||||
color: rgb(63, 127, 127);
|
||||
}
|
||||
|
||||
.str {
|
||||
color: rgb(128, 0, 0);
|
||||
}
|
||||
|
||||
.dig {
|
||||
color: rgb(128, 64, 0);
|
||||
}
|
||||
|
||||
.cmt {
|
||||
color: rgb(200, 0, 200);
|
||||
}
|
||||
|
||||
h2 {
|
||||
display: inline;
|
||||
font-weight: normal;
|
||||
}
|
||||
|
||||
.info {
|
||||
border-bottom: 1px solid #ddd;
|
||||
padding-bottom: 5px;
|
||||
margin-bottom: 20px;
|
||||
display: none;
|
||||
}
|
||||
|
||||
a {
|
||||
color: #9D2900;
|
||||
}
|
||||
|
||||
body {
|
||||
font-family: "Trebuchet MS", Arial, Sans-Serif;
|
||||
}
|
||||
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="info">
|
||||
<h2>testCrop.sikuli</h2> <a href="testCrop.zip">(Download this script)</a>
|
||||
</div>
|
||||
<pre class="sikuli-code">
|
||||
<span class="kw">def</span> setUp(self):
|
||||
<span class="kw">import</span> os
|
||||
ov_binany_path=os.environ[<span class="str">'OV_BINARY_PATH'</span>]
|
||||
self.terminal = App.open(<span class="str">"xterm -e "</span> + ov_binany_path +<span class="str">"/openvibe-designer.sh --no-session-management --play testOgreContext.xml"</span>)
|
||||
<span class="kw">while</span> <span class="kw">not</span> self.terminal.window():
|
||||
<span class="skw">wait</span>(<span class="dig">1</span>)
|
||||
<span class="skw">wait</span>(<img src="Simple3Dview.png" />,<span class="dig">10</span>)
|
||||
<span class="kw">def</span> testRunOgreVisual(self):
|
||||
waitVanish(<img src="Simple3Dview.png" />,<span class="dig">10</span>)
|
||||
<span class="kw">assert</span>(exists(<img src="designerScreen.png" />))
|
||||
<span class="kw">def</span> tearDown(self):
|
||||
<span class="kw">if</span> self.terminal.window():
|
||||
App.close(self.terminal)
|
||||
self.terminal= None
|
||||
</pre>
|
||||
</body>
|
||||
</html>
|
||||
+15
@@ -0,0 +1,15 @@
|
||||
def setUp(self):
|
||||
import os
|
||||
ov_binany_path=os.environ['OV_BINARY_PATH']
|
||||
self.terminal = App.open("xterm -e " + ov_binany_path +"/openvibe-designer.sh --no-session-management --play testOgreContext.xml")
|
||||
while not self.terminal.window():
|
||||
wait(1)
|
||||
wait("Simple3Dview.png",10)
|
||||
def testRunOgreVisual(self):
|
||||
waitVanish("Simple3Dview.png",10)
|
||||
assert(exists("designerScreen.png"))
|
||||
def tearDown(self):
|
||||
if self.terminal.window():
|
||||
App.close(self.terminal)
|
||||
self.terminal= None
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user