init
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/**
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* \page BoxAlgorithm_CSPSpatialFilterTrainer CSP Spatial Filter Trainer
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__________________________________________________________________
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Detailed description
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__________________________________________________________________
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* |OVP_DocBegin_BoxAlgorithm_CSPSpatialFilterTrainer_Description|
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This box computes spatial filters according to the Common Spatial Pattern algorithm. The goal of the algorithm is to improve the discrimination of two types of signals.
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The spatial filters are constructed in a way they maximize the variance for signals of the first condition while at the same time they minimize it for the second condition.
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This can be used for discriminating the signals of two commonly used motor-imagery tasks (e.g. left versus right hand movement).
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It can also be used for two-class SSVEP experiments or any other experiment where the discriminative information is contained in the variance (or power in a certain band) of the signal conditions.
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Please note that this implementation computes a <b>trace normalization</b>.
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* |OVP_DocEnd_BoxAlgorithm_CSPSpatialFilterTrainer_Description|
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__________________________________________________________________
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Inputs description
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__________________________________________________________________
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* |OVP_DocBegin_BoxAlgorithm_CSPSpatialFilterTrainer_Inputs|
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* |OVP_DocEnd_BoxAlgorithm_CSPSpatialFilterTrainer_Inputs|
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* |OVP_DocBegin_BoxAlgorithm_CSPSpatialFilterTrainer_Input1|
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This stimulus input is needed to indicate the end of a recording session (or end of file). It then triggers the training/computation of the CSP filters.
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* |OVP_DocEnd_BoxAlgorithm_CSPSpatialFilterTrainer_Input1|
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* |OVP_DocBegin_BoxAlgorithm_CSPSpatialFilterTrainer_Input2|
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This input expects epoched data for the first condition (e.g. epochs for left hand motor imagery).
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* |OVP_DocEnd_BoxAlgorithm_CSPSpatialFilterTrainer_Input2|
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* |OVP_DocBegin_BoxAlgorithm_CSPSpatialFilterTrainer_Input3|
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This input expects epoched data for the second condition (e.g. epochs for right hand motor imagery).
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* |OVP_DocEnd_BoxAlgorithm_CSPSpatialFilterTrainer_Input3|
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__________________________________________________________________
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Outputs description
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__________________________________________________________________
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* |OVP_DocBegin_BoxAlgorithm_CSPSpatialFilterTrainer_Outputs|
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* |OVP_DocEnd_BoxAlgorithm_CSPSpatialFilterTrainer_Outputs|
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* |OVP_DocBegin_BoxAlgorithm_CSPSpatialFilterTrainer_Output1|
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The CSP Trainer outputs the stimulation <b>OVTK_StimulationId_TrainCompleted</b> when the training process was successful. No output is produced if the process failed.
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* |OVP_DocEnd_BoxAlgorithm_CSPSpatialFilterTrainer_Output1|
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__________________________________________________________________
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Settings description
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__________________________________________________________________
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* |OVP_DocBegin_BoxAlgorithm_CSPSpatialFilterTrainer_Settings|
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* |OVP_DocEnd_BoxAlgorithm_CSPSpatialFilterTrainer_Settings|
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* |OVP_DocBegin_BoxAlgorithm_CSPSpatialFilterTrainer_Setting1|
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This should contain the stimulus identifier denoting the end of a recording session or end of file, e.g. OVTK_GDF_End_Of_Session or OVTK_StimulationId_ExperimentStop.
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* |OVP_DocEnd_BoxAlgorithm_CSPSpatialFilterTrainer_Setting1|
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* |OVP_DocBegin_BoxAlgorithm_CSPSpatialFilterTrainer_Setting2|
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This setting contains the path and filename of the configuration file in which the computed spatial filters are saved.
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* |OVP_DocEnd_BoxAlgorithm_CSPSpatialFilterTrainer_Setting2|
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* |OVP_DocBegin_BoxAlgorithm_CSPSpatialFilterTrainer_Setting3|
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Here you need to determine how many spatial filters will be computed (default value is two).
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* |OVP_DocEnd_BoxAlgorithm_CSPSpatialFilterTrainer_Setting3|
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* |OVP_DocBegin_BoxAlgorithm_CSPSpatialFilterTrainer_Setting4|
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If true, the output file will be a box configuration XML. Otherwise it will be a text format matrix.
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* |OVP_DocEnd_BoxAlgorithm_CSPSpatialFilterTrainer_Setting4|
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__________________________________________________________________
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Examples description
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__________________________________________________________________
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* |OVP_DocBegin_BoxAlgorithm_CSPSpatialFilterTrainer_Examples|
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Before training the spatial filter you should first filter the data with respect to the desired band (e.g. for motor imagery, certain people display good results in a narrow pass-band of 8-12Hz, others in 8-30Hz).
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As in the example scenario below, one could also opt for different pass-bands and compute filters in each of them, finally letting the subsequently trained classifier decide which features are important.
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\image html csp_training.png "Example scenario to compute CSP filters"
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Once the spatial filters are computed and saved in the configuration file, you will need to load it into the \ref Doc_BoxAlgorithm_SpatialFilter "Spatial Filter" box.
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* |OVP_DocEnd_BoxAlgorithm_CSPSpatialFilterTrainer_Examples|
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__________________________________________________________________
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Miscellaneous description
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__________________________________________________________________
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* |OVP_DocBegin_BoxAlgorithm_CSPSpatialFilterTrainer_Miscellaneous|
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For the moment it is only implemented for two classes. Multiple classes can be supported in the future according to an all-versus-one scheme or through joint diagonalization.
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* |OVP_DocEnd_BoxAlgorithm_CSPSpatialFilterTrainer_Miscellaneous|
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*/
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/**
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* \page BoxAlgorithm_Downsampling Downsampling -
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__________________________________________________________________
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Detailed description
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__________________________________________________________________
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* |OVP_DocBegin_BoxAlgorithm_Downsampling_Description|
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*
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* NOTE: This box has been deprecated. Please use
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* Signal Resampling box instead.
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*
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* This plugin is used to downsample the input signal. First, a
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* low-pass filter is applied to the input signal for anti-aliasing.
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* Then, the input signal is downsampled at the new sampling rate.
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* This plugin allows the selection of the kind of filter (Butterworth
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* or Chebyshev), the new sampling rate and the frequency
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* cutoff for the filter.
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*
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* |OVP_DocEnd_BoxAlgorithm_Downsampling_Description|
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__________________________________________________________________
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Inputs description
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__________________________________________________________________
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* |OVP_DocBegin_BoxAlgorithm_Downsampling_Inputs|
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* |OVP_DocEnd_BoxAlgorithm_Downsampling_Inputs|
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*
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* |OVP_DocBegin_BoxAlgorithm_Downsampling_Input1|
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* The input signal.
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* |OVP_DocEnd_BoxAlgorithm_Downsampling_Input1|
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__________________________________________________________________
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Outputs description
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__________________________________________________________________
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* |OVP_DocBegin_BoxAlgorithm_Downsampling_Outputs|
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* |OVP_DocEnd_BoxAlgorithm_Downsampling_Outputs|
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*
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* |OVP_DocBegin_BoxAlgorithm_Downsampling_Output1|
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* |OVP_DocEnd_BoxAlgorithm_Downsampling_Output1|
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__________________________________________________________________
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Settings description
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__________________________________________________________________
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* |OVP_DocBegin_BoxAlgorithm_Downsampling_Settings|
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* |OVP_DocEnd_BoxAlgorithm_Downsampling_Settings|
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*
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* |OVP_DocBegin_BoxAlgorithm_Downsampling_Setting1|
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* New sampling rate in Hz chosen to downsample the input signal.
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* |OVP_DocEnd_BoxAlgorithm_Downsampling_Setting1|
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*
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* |OVP_DocBegin_BoxAlgorithm_Downsampling_Setting2|
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* Select the frequency cutoff of the low-pass filter as a ratio (1/2,
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* 1/3 or 1/4) of the new sampling rate.
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* |OVP_DocEnd_BoxAlgorithm_Downsampling_Setting2|
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*
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* |OVP_DocBegin_BoxAlgorithm_Downsampling_Setting3|
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* Select the kind of filter between Butterworth and Chebyshev.
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* |OVP_DocEnd_BoxAlgorithm_Downsampling_Setting3|
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*
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* |OVP_DocBegin_BoxAlgorithm_Downsampling_Setting4|
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* Order of the low-pass filter.
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* |OVP_DocEnd_BoxAlgorithm_Downsampling_Setting4|
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*
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* |OVP_DocBegin_BoxAlgorithm_Downsampling_Setting5|
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* If Chebyshev filter is selected, PassBand Ripple is a necessary info.
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* |OVP_DocEnd_BoxAlgorithm_Downsampling_Setting5|
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__________________________________________________________________
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Examples description
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__________________________________________________________________
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* |OVP_DocBegin_BoxAlgorithm_Downsampling_Examples|
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* Let's consider our input signal sampling rate is 1 kHz.
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* If the new selected sampling rate is 200 Hz and the Frequency
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* cutoff ratio is 1/4, then a low-pass filter (with frequency
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* cutoff equal to 200*1/4 = 50 Hz) is applied before downsampling
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* at 200 Hz.
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* |OVP_DocEnd_BoxAlgorithm_Downsampling_Examples|
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__________________________________________________________________
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Miscellaneous description
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__________________________________________________________________
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* |OVP_DocBegin_BoxAlgorithm_Downsampling_Miscellaneous|
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* This plugin downsamples the input signal and previously realizes
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* an anti-aliasing filtering.
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* |OVP_DocEnd_BoxAlgorithm_Downsampling_Miscellaneous|
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*/
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/**
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* \page BoxAlgorithm_IndependentComponentAnalysisFastICA Independent Component Analysis (FastICA)
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__________________________________________________________________
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Detailed description
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__________________________________________________________________
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* |OVP_DocBegin_BoxAlgorithm_IndependentComponentAnalysisFastICA_Description|
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* This box attempts to find a decomposition of the signal to its
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* independent components. The approach is based on the FastICA algorithm.
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Description|
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__________________________________________________________________
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Inputs description
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__________________________________________________________________
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* |OVP_DocBegin_BoxAlgorithm_IndependentComponentAnalysisFastICA_Inputs|
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Inputs|
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*
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* |OVP_DocBegin_BoxAlgorithm_IndependentComponentAnalysisFastICA_Input1|
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* The input signal.
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Input1|
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__________________________________________________________________
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Outputs description
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__________________________________________________________________
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* |OVP_DocBegin_BoxAlgorithm_IndependentComponentAnalysisFastICA_Outputs|
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Outputs|
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*
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* |OVP_DocBegin_BoxAlgorithm_IndependentComponentAnalysisFastICA_Output1|
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* The decomposed signal.
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Output1|
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__________________________________________________________________
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Settings description
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__________________________________________________________________
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* |OVP_DocBegin_BoxAlgorithm_IndependentComponentAnalysisFastICA_Settings|
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Settings|
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*
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* |OVP_DocBegin_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting1|
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* Number of independent components to extract (equals PCA dimension reduction)
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting1|
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*
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* |OVP_DocBegin_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting2|
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* Which decomposition is desired?
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting2|
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*
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* |OVP_DocBegin_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting3|
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* How many seconds of sample to collect to estimate the ICA model?
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting3|
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*
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* |OVP_DocBegin_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting4|
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* Decomposition type. Deflation is an approach where each component is
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* estimated separately in turns. Symmetric estimation optimizes all
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* components at once.
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting4|
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*
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* |OVP_DocBegin_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting5|
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* Maximum number of iterations
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting5|
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*
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* |OVP_DocBegin_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting6|
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* Enable fine tuning?
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting6|
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*
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* |OVP_DocBegin_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting7|
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* Maximum number of iterations for the fine tuning
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting7|
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*
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* |OVP_DocBegin_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting8|
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* Used nonlinearity type
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting8|
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*
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* |OVP_DocBegin_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting9|
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* Mu parameter
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting9|
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*
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* |OVP_DocBegin_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting10|
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* Epsilon parameter
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting10|
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*
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* |OVP_DocBegin_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting11|
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* Filename to save the estimated decomposition matrix W to
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting11|
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*
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* |OVP_DocBegin_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting12|
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* Should the matrix W be saved to a file?
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Setting12|
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*
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Settings|
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__________________________________________________________________
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Examples description
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__________________________________________________________________
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* |OVP_DocBegin_BoxAlgorithm_IndependentComponentAnalysisFastICA_Examples|
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* One use-case of ICA is to attempt to separate the signal of interest from
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* nuisance artifacts. For example, supposing that ICA makes a meaningful decomposition
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* of your EEG signal, you will see artifacts such as those from eyeblinks more
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* clearly segregated to specific output channels instead of contaminating
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* all of the channels.
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Examples|
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__________________________________________________________________
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Miscellaneous description
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__________________________________________________________________
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* |OVP_DocBegin_BoxAlgorithm_IndependentComponentAnalysisFastICA_Miscellaneous|
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* This plugin applies the FastICA algorithm to the input signal. The box can store the
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* estimated decomposition matrix W to a file. This file can then be used later
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* in the spatial filter box to apply the decomposition on fresh data.
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*
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* The box also outputs the decomposed signal, but the decomposition is active only
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* after the model has been estimated (after the specified number of samples have been collected).
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* If you wish to decompose the whole data, then you can first train the ICA model, save the matrix,
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* and then separately apply it to the original data with the spatial filter.
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*
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* The FastICA algorithm is described in
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*
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* A. Hyvärinen. "Fast and Robust Fixed-Point Algorithms for Independent Component Analysis", IEEE Transactions on Neural Networks 10(3):626-634, 1999.
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*
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* The implementation used by the box is from the ITPP toolkit.
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*
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* |OVP_DocEnd_BoxAlgorithm_IndependentComponentAnalysisFastICA_Miscellaneous|
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*/
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/**
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* \page BoxAlgorithm_Min_MaxDetection Min/Max detection -
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__________________________________________________________________
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|
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Detailed description
|
||||
__________________________________________________________________
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||||
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* |OVP_DocBegin_BoxAlgorithm_Min_MaxDetection_Description|
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* This plugin is used to detect the minimum or the maximum value between
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* 2 dates. This plugin allows the selection of the minimum or the maximum
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* value to detect and the time start and time stop in between you are looking at.
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* |OVP_DocEnd_BoxAlgorithm_Min_MaxDetection_Description|
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__________________________________________________________________
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Inputs description
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__________________________________________________________________
|
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|
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* |OVP_DocBegin_BoxAlgorithm_Min_MaxDetection_Inputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_Min_MaxDetection_Inputs|
|
||||
*
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* |OVP_DocBegin_BoxAlgorithm_Min_MaxDetection_Input1|
|
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* The input signal.
|
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* |OVP_DocEnd_BoxAlgorithm_Min_MaxDetection_Input1|
|
||||
__________________________________________________________________
|
||||
|
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Outputs description
|
||||
__________________________________________________________________
|
||||
|
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* |OVP_DocBegin_BoxAlgorithm_Min_MaxDetection_Outputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_Min_MaxDetection_Outputs|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_Min_MaxDetection_Output1|
|
||||
* |OVP_DocEnd_BoxAlgorithm_Min_MaxDetection_Output1|
|
||||
__________________________________________________________________
|
||||
|
||||
Settings description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_Min_MaxDetection_Settings|
|
||||
* |OVP_DocEnd_BoxAlgorithm_Min_MaxDetection_Settings|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_Min_MaxDetection_Setting1|
|
||||
* Select if you want to detect the Min or the Max.
|
||||
* |OVP_DocEnd_BoxAlgorithm_Min_MaxDetection_Setting1|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_Min_MaxDetection_Setting2|
|
||||
* Starting time the algorithm will search the Min/Max value.
|
||||
* |OVP_DocEnd_BoxAlgorithm_Min_MaxDetection_Setting2|
|
||||
*
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||||
* |OVP_DocBegin_BoxAlgorithm_Min_MaxDetection_Setting3|
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* Ending time the algorithm will search the Min/Max value.
|
||||
* |OVP_DocEnd_BoxAlgorithm_Min_MaxDetection_Setting3|
|
||||
__________________________________________________________________
|
||||
|
||||
Examples description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_Min_MaxDetection_Examples|
|
||||
* Let's consider our input signal is an ERP.
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||||
* To detect the P300 ERP, select Max value and a Time Window Start equal
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||||
* to 250 ms and a Time Window End equal to 450 ms.
|
||||
* |OVP_DocEnd_BoxAlgorithm_Min_MaxDetection_Examples|
|
||||
__________________________________________________________________
|
||||
|
||||
Miscellaneous description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_Min_MaxDetection_Miscellaneous|
|
||||
* This plugin detects the minimum or the maximum value in a time window interval.
|
||||
* |OVP_DocEnd_BoxAlgorithm_Min_MaxDetection_Miscellaneous|
|
||||
*/
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+86
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/**
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||||
* \page BoxAlgorithm_ModifiableTemporalFilter Modifiable Temporal filter -
|
||||
__________________________________________________________________
|
||||
|
||||
Detailed description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ModifiableTemporalFilter_Description|
|
||||
* This plugin is used to filter the input signal. This plugin allows
|
||||
* the selection of the kind of filter (Butterworth or Chebyshev),
|
||||
* the kind of filter (low pass, high pass, band pass, band stop), the low
|
||||
* or/and the high passband edge and the passband ripple for the Chebyshev filter.
|
||||
* This box is a duplicate of the Temporal Filter box with all its settings marked as modifiable.
|
||||
* Each time a setting changes, the box recompute the filter coefficients accordingly.
|
||||
* |OVP_DocEnd_BoxAlgorithm_ModifiableTemporalFilter_Description|
|
||||
__________________________________________________________________
|
||||
|
||||
Inputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ModifiableTemporalFilter_Inputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_ModifiableTemporalFilter_Inputs|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_ModifiableTemporalFilter_Input1|
|
||||
* The input signal.
|
||||
* |OVP_DocEnd_BoxAlgorithm_ModifiableTemporalFilter_Input1|
|
||||
__________________________________________________________________
|
||||
|
||||
Outputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ModifiableTemporalFilter_Outputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_ModifiableTemporalFilter_Outputs|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_ModifiableTemporalFilter_Output1|
|
||||
* |OVP_DocEnd_BoxAlgorithm_ModifiableTemporalFilter_Output1|
|
||||
__________________________________________________________________
|
||||
|
||||
Settings description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ModifiableTemporalFilter_Settings|
|
||||
* |OVP_DocEnd_BoxAlgorithm_ModifiableTemporalFilter_Settings|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_ModifiableTemporalFilter_Setting1|
|
||||
* Select the name of filter between Butterworth and Chebyshev. Modifiable online.
|
||||
* |OVP_DocEnd_BoxAlgorithm_ModifiableTemporalFilter_Setting1|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_ModifiableTemporalFilter_Setting2|
|
||||
* Select the kind of filter between Low pass, High pass, Band pass, Band stop. Modifiable online.
|
||||
* |OVP_DocEnd_BoxAlgorithm_ModifiableTemporalFilter_Setting2|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_ModifiableTemporalFilter_Setting3|
|
||||
* Order of the low-pass filter. Modifiable online.
|
||||
* |OVP_DocEnd_BoxAlgorithm_ModifiableTemporalFilter_Setting3|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_ModifiableTemporalFilter_Setting4|
|
||||
* Low edge for High pass, Band pass and Band stop filters. Modifiable online.
|
||||
* |OVP_DocEnd_BoxAlgorithm_ModifiableTemporalFilter_Setting4|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_ModifiableTemporalFilter_Setting5|
|
||||
* High edge for Low pass, Band pass and Band stop filters. Modifiable online.
|
||||
* |OVP_DocEnd_BoxAlgorithm_ModifiableTemporalFilter_Setting5|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_ModifiableTemporalFilter_Setting6|
|
||||
* If Chebyshev filter is selected, PassBand Ripple is a necessary info. Modifiable online.
|
||||
* |OVP_DocEnd_BoxAlgorithm_ModifiableTemporalFilter_Setting6|
|
||||
__________________________________________________________________
|
||||
|
||||
Examples description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ModifiableTemporalFilter_Examples|
|
||||
* Let's consider our input signal is very noisy (50 Hz).
|
||||
* To filter this signal, select a Low pass Butterworth filter of 4th
|
||||
* order and High PassBand Edge equal to 30 Hz for example.
|
||||
* |OVP_DocEnd_BoxAlgorithm_ModifiableTemporalFilter_Examples|
|
||||
__________________________________________________________________
|
||||
|
||||
Miscellaneous description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_ModifiableTemporalFilter_Miscellaneous|
|
||||
* This plugin filters the input signal. Several filtering are available.
|
||||
* |OVP_DocEnd_BoxAlgorithm_ModifiableTemporalFilter_Miscellaneous|
|
||||
*/
|
||||
+75
@@ -0,0 +1,75 @@
|
||||
/**
|
||||
* \page BoxAlgorithm_SpectralAnalysisFFTINSERMContrib Spectral analysis -
|
||||
__________________________________________________________________
|
||||
|
||||
Detailed description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Description|
|
||||
The Spectral Analysis box performs spectrum computations on incoming signals and possible outputs include the spectrum amplitude (the power of the signal in a number of frequency bands), as well as its phase, real part and imaginary part. Output computations may be enabled/disabled from the settings dialog box. The analysis is performed using a <b> Fast Fourier Transform </b>.
|
||||
* |OVP_DocEnd_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Description|
|
||||
__________________________________________________________________
|
||||
|
||||
Inputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Inputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Inputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Input1|
|
||||
The input signal.
|
||||
* |OVP_DocEnd_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Input1|
|
||||
__________________________________________________________________
|
||||
|
||||
Outputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Outputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Outputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Output1|
|
||||
Amplitude of input signal in frequency bands.
|
||||
* |OVP_DocEnd_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Output1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Output2|
|
||||
Phase of input signal
|
||||
* |OVP_DocEnd_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Output2|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Output3|
|
||||
Real part of input signal
|
||||
* |OVP_DocEnd_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Output3|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Output4|
|
||||
Imaginary part of input signal
|
||||
* |OVP_DocEnd_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Output4|
|
||||
__________________________________________________________________
|
||||
|
||||
Settings description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Settings|
|
||||
* |OVP_DocEnd_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Settings|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Setting1|
|
||||
* |OVP_DocEnd_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Setting1|
|
||||
__________________________________________________________________
|
||||
|
||||
Examples description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Examples|
|
||||
Practical example : visualizing the power spectrum of a signal.
|
||||
|
||||
Let's use a Signal Oscillator box to generator sinusoidal signals on one channel. Next we add a Spectral Analysis box and connect boxes together. We make sure the 'Amplitude' of the signal is computed by checking the appropriate setting in the settings dialog box (see image below). Finally, we connect the 'Amplitude' output connector of the Spectral Analysis box to the input connector of a Power Spectrum Display box. The player may now be launched to visualize the power spectrum of the signal.
|
||||
|
||||
\image html SpectralAnalysisFFTINSERMContrib_online.png "Visualizing the power spectrum of sinusoidal signals."
|
||||
* |OVP_DocEnd_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Examples|
|
||||
|
||||
__________________________________________________________________
|
||||
|
||||
Miscellaneous description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Miscellaneous|
|
||||
* |OVP_DocEnd_BoxAlgorithm_SpectralAnalysisFFTINSERMContrib_Miscellaneous|
|
||||
*/
|
||||
+90
@@ -0,0 +1,90 @@
|
||||
/**
|
||||
* \page BoxAlgorithm_TemporalFilterINSERMContrib Temporal filter -
|
||||
__________________________________________________________________
|
||||
|
||||
Detailed description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_TemporalFilterINSERMContrib_Description|
|
||||
* This plugin is used to filter the input signal. This plugin allows
|
||||
* the selection of the kind of filter (Butterworth or Chebyshev),
|
||||
* the kind of filter (low pass, high pass, band pass, band stop), the low
|
||||
* or/and the high passband edge and the passband ripple for the Chebyshev filter.
|
||||
* |OVP_DocEnd_BoxAlgorithm_TemporalFilterINSERMContrib_Description|
|
||||
__________________________________________________________________
|
||||
|
||||
Inputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_TemporalFilterINSERMContrib_Inputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_TemporalFilterINSERMContrib_Inputs|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_TemporalFilterINSERMContrib_Input1|
|
||||
* The input signal.
|
||||
* |OVP_DocEnd_BoxAlgorithm_TemporalFilterINSERMContrib_Input1|
|
||||
__________________________________________________________________
|
||||
|
||||
Outputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_TemporalFilterINSERMContrib_Outputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_TemporalFilterINSERMContrib_Outputs|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_TemporalFilterINSERMContrib_Output1|
|
||||
* |OVP_DocEnd_BoxAlgorithm_TemporalFilterINSERMContrib_Output1|
|
||||
__________________________________________________________________
|
||||
|
||||
Settings description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_TemporalFilterINSERMContrib_Settings|
|
||||
* |OVP_DocEnd_BoxAlgorithm_TemporalFilterINSERMContrib_Settings|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_TemporalFilterINSERMContrib_Setting1|
|
||||
* Select the name of filter between Butterworth and Chebyshev.
|
||||
* |OVP_DocEnd_BoxAlgorithm_TemporalFilterINSERMContrib_Setting1|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_TemporalFilterINSERMContrib_Setting2|
|
||||
* Select the kind of filter between Low pass, High pass, Band pass, Band stop.
|
||||
* |OVP_DocEnd_BoxAlgorithm_TemporalFilterINSERMContrib_Setting2|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_TemporalFilterINSERMContrib_Setting3|
|
||||
* Order of the low-pass filter.
|
||||
* |OVP_DocEnd_BoxAlgorithm_TemporalFilterINSERMContrib_Setting3|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_TemporalFilterINSERMContrib_Setting4|
|
||||
* Low edge for High pass, Band pass and Band stop filters
|
||||
* |OVP_DocEnd_BoxAlgorithm_TemporalFilterINSERMContrib_Setting4|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_TemporalFilterINSERMContrib_Setting5|
|
||||
* High edge for Low pass, Band pass and Band stop filters
|
||||
* |OVP_DocEnd_BoxAlgorithm_TemporalFilterINSERMContrib_Setting5|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_TemporalFilterINSERMContrib_Setting6|
|
||||
* If Chebyshev filter is selected, PassBand Ripple is a necessary info.
|
||||
* |OVP_DocEnd_BoxAlgorithm_TemporalFilterINSERMContrib_Setting6|
|
||||
__________________________________________________________________
|
||||
|
||||
Examples description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_TemporalFilterINSERMContrib_Examples|
|
||||
* Let's consider our input signal is very noisy (50 Hz).
|
||||
* To filter this signal, select a Low pass Butterworth filter of 4th
|
||||
* order and High PassBand Edge equal to 30 Hz for example.
|
||||
* |OVP_DocEnd_BoxAlgorithm_TemporalFilterINSERMContrib_Examples|
|
||||
__________________________________________________________________
|
||||
|
||||
Miscellaneous description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_TemporalFilterINSERMContrib_Miscellaneous|
|
||||
* This plugin filters the input signal. Several filtering are available.
|
||||
*
|
||||
* The box is able to use filter orders that are larger than the input chunk
|
||||
* size. However, with high order filters in general, remember to check that the
|
||||
* filtered output of the box remains stable and meaningful. If not,
|
||||
* try decreasing the filter order.
|
||||
*
|
||||
* |OVP_DocEnd_BoxAlgorithm_TemporalFilterINSERMContrib_Miscellaneous|
|
||||
*/
|
||||
+143
@@ -0,0 +1,143 @@
|
||||
/**
|
||||
* \page BoxAlgorithm_UnivariateStatistics Univariate Statistics
|
||||
__________________________________________________________________
|
||||
|
||||
Detailed description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_UnivariateStatistics_Description|
|
||||
* This plugin computes the mean, variance, range, median, Inter-
|
||||
* Quantile-Range and Percentile of each incoming sample
|
||||
* buffer (or chunk) and outputs the resulting signals.
|
||||
* |OVP_DocEnd_BoxAlgorithm_UnivariateStatistics_Description|
|
||||
__________________________________________________________________
|
||||
|
||||
Inputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_UnivariateStatistics_Inputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_UnivariateStatistics_Inputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_UnivariateStatistics_Input1|
|
||||
* The input signal.
|
||||
* |OVP_DocEnd_BoxAlgorithm_UnivariateStatistics_Input1|
|
||||
__________________________________________________________________
|
||||
|
||||
Outputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_UnivariateStatistics_Outputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_UnivariateStatistics_Outputs|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_UnivariateStatistics_Output1|
|
||||
* Signal containing the averages of the input sample buffers.
|
||||
* |OVP_DocEnd_BoxAlgorithm_UnivariateStatistics_Output1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_UnivariateStatistics_Output2|
|
||||
* Signal containing the variance of the input sample buffers.
|
||||
* Sample variance is a measure of the spread of or dispersion
|
||||
* within a set of sample data.
|
||||
* The sample variance is the sum of the squared deviations
|
||||
* from their average divided by the number of observations in
|
||||
* the data set.
|
||||
* |OVP_DocEnd_BoxAlgorithm_UnivariateStatistics_Output2|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_UnivariateStatistics_Output3|
|
||||
* Signal containing the range of the input sample buffers.
|
||||
* The range of a sample (or a data set) is a measure of the
|
||||
* spread or the dispersion of the observations. It is the
|
||||
* difference between the largest and the smallest observed
|
||||
* value of some quantitative characteristic.
|
||||
* |OVP_DocEnd_BoxAlgorithm_UnivariateStatistics_Output3|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_UnivariateStatistics_Output4|
|
||||
* Signal containing the median of the input sample buffers.
|
||||
* The median is the value halfway through the ordered data
|
||||
* set, below and above which there lies an equal number of
|
||||
* data values.
|
||||
* |OVP_DocEnd_BoxAlgorithm_UnivariateStatistics_Output4|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_UnivariateStatistics_Output5|
|
||||
* Signal containing the Inter-Quantile-Range of the input
|
||||
* sample buffers.
|
||||
* The inter-quartile range is a measure of the spread of or
|
||||
* dispersion within a data set.
|
||||
* It is calculated by taking the difference between the upper
|
||||
* and the lower quartiles.
|
||||
* |OVP_DocEnd_BoxAlgorithm_UnivariateStatistics_Output5|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_UnivariateStatistics_Output6|
|
||||
* Signal containing the percentile of the input sample buffers.
|
||||
* Percentiles are values that divide a sample of data into one
|
||||
* hundred groups containing (as far as possible) equal numbers
|
||||
* of observations. For example, 30% of the data values lie below
|
||||
* the 30th percentile.
|
||||
* |OVP_DocEnd_BoxAlgorithm_UnivariateStatistics_Output6|
|
||||
|
||||
__________________________________________________________________
|
||||
|
||||
Settings description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_UnivariateStatistics_Settings|
|
||||
* |OVP_DocEnd_BoxAlgorithm_UnivariateStatistics_Settings|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_UnivariateStatistics_Setting1|
|
||||
* Mean activation. If the box is checked, the mean is computed
|
||||
* and the mean output send signal
|
||||
* |OVP_DocEnd_BoxAlgorithm_UnivariateStatistics_Setting1|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_UnivariateStatistics_Setting2|
|
||||
* Variance activation. If the box is checked, the variance is computed
|
||||
* and the variance output send signal
|
||||
* |OVP_DocEnd_BoxAlgorithm_UnivariateStatistics_Setting2|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_UnivariateStatistics_Setting3|
|
||||
* Range activation. If the box is checked, the range is computed
|
||||
* and the range output send signal
|
||||
* |OVP_DocEnd_BoxAlgorithm_UnivariateStatistics_Setting3|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_UnivariateStatistics_Setting4|
|
||||
* Median activation. If the box is checked, the median is computed
|
||||
* and the median output send signal
|
||||
* |OVP_DocEnd_BoxAlgorithm_UnivariateStatistics_Setting4|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_UnivariateStatistics_Setting5|
|
||||
* Inter-Quantile-Range (IQR) activation. If the box is checked,
|
||||
* the IQR is computed and the IQR output send signal
|
||||
* |OVP_DocEnd_BoxAlgorithm_UnivariateStatistics_Setting5|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_UnivariateStatistics_Setting6|
|
||||
* Percentile activation. If the box is checked, the percentile
|
||||
* is computed and the percentile output send signal
|
||||
* |OVP_DocEnd_BoxAlgorithm_UnivariateStatistics_Setting6|
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_UnivariateStatistics_Setting7|
|
||||
* Percentile value. Change the percentile value for percentile
|
||||
* signal output
|
||||
* |OVP_DocEnd_BoxAlgorithm_UnivariateStatistics_Setting7|
|
||||
|
||||
__________________________________________________________________
|
||||
|
||||
Examples description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_UnivariateStatistics_Examples|
|
||||
* |OVP_DocEnd_BoxAlgorithm_UnivariateStatistics_Examples|
|
||||
__________________________________________________________________
|
||||
|
||||
Miscellaneous description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_UnivariateStatistics_Miscellaneous|
|
||||
* The output signal's sample count per channel per buffer is one,
|
||||
* since a buffer contains the averages (per channel) of the values
|
||||
* of an input buffer.
|
||||
* Be careful of Down-sampling effect for signal, the sampling rate
|
||||
* at output is divided by the number of input samples. This information
|
||||
* is saved on integer, so this stay true only if division is without
|
||||
* fractional part (or the subsampling should be a divisor of the number
|
||||
* of samples)
|
||||
* http://www.stats.gla.ac.uk/steps/glossary/presenting_data.html
|
||||
* |OVP_DocEnd_BoxAlgorithm_UnivariateStatistics_Miscellaneous|
|
||||
*/
|
||||
+66
@@ -0,0 +1,66 @@
|
||||
/**
|
||||
* \page BoxAlgorithm_WindowingINSERMContrib Windowing functions -
|
||||
__________________________________________________________________
|
||||
|
||||
Detailed description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_WindowingINSERMContrib_Description|
|
||||
* This plugin is used to apply a window to the input signal.
|
||||
* This plugin allows the selection of the kind of window
|
||||
* (Hamming, Hanning, Hann, Blackman, Triangular, Square Root).
|
||||
* |OVP_DocEnd_BoxAlgorithm_WindowingINSERMContrib_Description|
|
||||
__________________________________________________________________
|
||||
|
||||
Inputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_WindowingINSERMContrib_Inputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_WindowingINSERMContrib_Inputs|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_WindowingINSERMContrib_Input1|
|
||||
* |OVP_DocEnd_BoxAlgorithm_WindowingINSERMContrib_Input1|
|
||||
__________________________________________________________________
|
||||
|
||||
Outputs description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_WindowingINSERMContrib_Outputs|
|
||||
* |OVP_DocEnd_BoxAlgorithm_WindowingINSERMContrib_Outputs|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_WindowingINSERMContrib_Output1|
|
||||
* |OVP_DocEnd_BoxAlgorithm_WindowingINSERMContrib_Output1|
|
||||
__________________________________________________________________
|
||||
|
||||
Settings description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_WindowingINSERMContrib_Settings|
|
||||
* |OVP_DocEnd_BoxAlgorithm_WindowingINSERMContrib_Settings|
|
||||
*
|
||||
* |OVP_DocBegin_BoxAlgorithm_WindowingINSERMContrib_Setting1|
|
||||
* Select the name of window between Hamming, Hanning, Hann,
|
||||
* Blackman, Triangular and Square Root.
|
||||
* |OVP_DocEnd_BoxAlgorithm_WindowingINSERMContrib_Setting1|
|
||||
__________________________________________________________________
|
||||
|
||||
Examples description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_WindowingINSERMContrib_Examples|
|
||||
* Let's consider our input signal.
|
||||
* To prevent rebound in spectrum analysis due to the square root
|
||||
* windowing, select a Hanning window for example.
|
||||
* |OVP_DocEnd_BoxAlgorithm_WindowingINSERMContrib_Examples|
|
||||
__________________________________________________________________
|
||||
|
||||
Miscellaneous description
|
||||
__________________________________________________________________
|
||||
|
||||
* |OVP_DocBegin_BoxAlgorithm_WindowingINSERMContrib_Miscellaneous|
|
||||
* This plugin applies a window (weighting function) to each of the channels of the
|
||||
* input signal. Several windows are available. Note that the window is applied
|
||||
* separately per each chunk and each channel. It does not 'slide' over the streaming data.
|
||||
* |OVP_DocEnd_BoxAlgorithm_WindowingINSERMContrib_Miscellaneous|
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*/
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