BCIgui/Masterarbeit/openvibe/designer-master/plugins/visualization/ovp-advanced-visualization/doc/advviz-2-concepts.dox

/** 
  \page Doc_Mensia_AdvViz_Concepts Concepts
  
  \section Doc_Mensia_AdvViz_Concepts_Intro Introduction 
  The <b>Mensia Advanced Visualization Toolset</b> is a collection of boxes dedicated to 
  the visualization of the result of electrophysiological signal analysis, and are especially suitable for the <b> real-time
  analysis of EEG signals </b>, from raw signal display to 3D source reconstruction.
  
  It addresses many different use-cases among users. 
  Neurophysiologists can observe accurately in real-time <b> spatial and
  temporal patterns </b> in the brain activity (motor activity, cognitive processes). EEG signal
  processing specialists can <b>evaluate and compare</b> instantly algorithms effects (source
  separation, denoising techniques). BCI researchers can study how their ERP-based system may
  be tuned to elicit and detect the best brain response.
  
  
  \image html designer-box-list.png "Simple integration in the graphical user interface"
  \image latex designer-box-list.png "Simple integration in the graphical user interface" width=\textwidth
 
  \section Doc_Mensia_AdvViz_Concepts_VisualizationParadigms Visualization paradigms
  This Toolset has been designed to be very versatile. The main design concept revolves around
  the data presentation. You basically want to display matrices of numbers which may have temporal,
  and/or spatial meanings. The most adapted data presentation may vary from one case to another, 
  according to the type of events or patterns on which you need to get a good contrast.
  
  Before choosing the right visualization box, ask yourself:
  - How do I want my data to be displayed ? curves ? levels ?
  - What will be the best way to <b> enhance the contrast </b> between the information I want to extract and the rest of the data ?
  - Is my data stream <b>continuous</b> in time ? or am I dealing with discontinuous epochs (e.g. ERPs) ?
  
  To be adapted in most situation, the Mensia Advanced Visualization Toolset has been designed to cover
  different visualization paradigms. Take a look at all the possibilities and choose what will best fit your needs.
  - \ref Doc_Mensia_AdvViz_Concepts_VisualizationParadigms_Oscilloscope
  - \ref Doc_Mensia_AdvViz_Concepts_VisualizationParadigms_Bars
  - \ref Doc_Mensia_AdvViz_Concepts_VisualizationParadigms_Bitmap
  - \ref Doc_Mensia_AdvViz_Concepts_VisualizationParadigms_Topo
  - \ref Doc_Mensia_AdvViz_Concepts_VisualizationParadigms_Reco
  
  You can also have a look at the \ref Doc_Mensia_AdvViz_UseCases "list of use-cases", showing how each box can be used on concrete, real-life examples.
  
  \subsection Doc_Mensia_AdvViz_Concepts_VisualizationParadigms_Oscilloscope The Oscilloscope view
  It is the most basic paradigm, used to display temporal numerical data in the form of <b> curves </b> (dots linked by lines).
  The Oscilloscope views are all expecting <b>centered</b> values (i.e. distributed around 0).
  Hence it is advised to use at least one temporal filter (e.g. band passing between 2 and 40 Hz using a \ref Doc_BoxAlgorithm_TemporalFilter box) before displaying an EEG signal.
  
  Four boxes use this paradigm:
  - <b> \ref Doc_BoxAlgorithm_ContinuousOscilloscope </b> box: displays continuous data from left to right on a defined horizontal scale (goes back to origin upon reaching the end of the scale),
  channels are displayed vertically one after another, but spikes may overlap. 
  - <b> \ref Doc_BoxAlgorithm_InstantOscilloscope </b> box: displays each block of data received as it comes, filling all the horizontal space available.
  - <b> \ref Doc_BoxAlgorithm_ContinuousMultiOscilloscope </b> box: same as the Continuous Oscilloscope, but every input channels are displayed along the same horizontal axis with a different color, additively.
  - <b> \ref Doc_BoxAlgorithm_InstantMultiOscilloscope </b> box: same as the Instant Oscilloscope, but every input channels are displayed along the same horizontal axis with a different color, additively.
   
  <b>Example</b>: raw EEG signal display.
  \image html ContinuousOscilloscope_Display.png "Continuous Oscilloscope displaying 2 EEG channels"
  \image latex ContinuousOscilloscope_Display.png "Continuous Oscilloscope displaying 2 EEG channels" width=10cm
  
  \subsection Doc_Mensia_AdvViz_Concepts_VisualizationParadigms_Bars The Bar view
  Like histograms, this paradigm can be used to display and compare <b> series of levels </b> . Levels are displayed one after another from left to right, within a <b> color gradient </b> .
  Channels are displayed vertically, one after another with a fixed interval (thus some "high" levels may overlap).
  With a high definition (i.e. a rather high frequency display), the result can be viewed as a curve colored below the line.
  
  Two boxes uses this paradigm:
  - <b> \ref Doc_BoxAlgorithm_ContinuousBars </b> box: displays continuous data from left to right on a defined horizontal scale (goes back to origin upon reaching the end of the scale).
  - <b> \ref Doc_BoxAlgorithm_InstantBars </b> box: displays each block of data received as it comes, filling all the horizontal space.
   
  <b>Example</b>: spectrum display.
  \image html InstantBars_Display.png "Instant Bars displaying the signal spectrum"
  \image latex InstantBars_Display.png "Instant Bars displaying the signal spectrum" width=10cm
   
  \subsection Doc_Mensia_AdvViz_Concepts_VisualizationParadigms_Bitmap The Bitmap view
  The bitmap paradigm displays matrices of data using a color gradient. The result is a <b> 2D map where each cell is given a color "bit" </b> .
  This view using colors can enhance easily the constrast between 2 temporal or spatial patterns, as the difference
  between "cold" and "hot" colors is quickly caught by the analyst's eye.
  You can even add an additional dimension by using <b> stacked bitmaps </b> : every time a new bitmap is received, it is placed on top or left to the previous one.
  
  Four boxes uses this paradigm:
  - <b> \ref Doc_BoxAlgorithm_ContinuousBitmap </b> box: displays continuous data from left to right on a defined  horizontal scale (goes back to origin upon reaching the end of the scale).
  - <b> \ref Doc_BoxAlgorithm_InstantBitmap </b> box: displays each block of data received as it comes, filling all the horizontal space.
  - <b> \ref Doc_BoxAlgorithm_StackedBitmapVertical </b> box: each bitmap is placed on <b> top </b> of the previous one.
  - <b> \ref Doc_BoxAlgorithm_StackedBitmapHorizontal </b> box: each bitmap is placed <b> left </b> to the previous one.
  
  <b>Example</b>: Time-frequency map.
  \image html StackedBitmapHorz_Display.png "Stacked Bitmap (Horizontal) displaying the result of a Time-Frequency analysis"
  \image latex StackedBitmapHorz_Display.png "Stacked Bitmap (Horizontal) displaying the result of a Time-Frequency analysis" width=10cm
   
  \subsection Doc_Mensia_AdvViz_Concepts_VisualizationParadigms_Topo The Topographic view
  This paradigm adds a strong spatial constraint on the input data: each channel must be <b> labelled
  with an electrode name </b> in a defined nomenclature, such as the standard 10-20 system. 
  Please see \ref Doc_Mensia_AdvViz_Concepts_ChannelLocalization for further details.
  
  Here again the data itself is displayed using a color gradient, mapped to a 2D or 3D model using <b> spherical spline interpolation</b>.
  For more details about the spherical spline interpolation, please check <i>F. Perrin, J. Pernier, O. Bertrand, J.F. Echallier, 
  Spherical splines for scalp potential and current density mapping, Electroencephalography and Clinical Neurophysiology, Volume 72, Issue 2, February 1989, Pages 184-187</i>.

    The 2D model is a planar projection of the scalp, covering the scalp roughly from the frontal area to the occipital area (i.e. from Fp1-Fp2 to O9-O10 sites).
  The projection result takes the shape of a disk with a crescent growth at the back for the occipital region.
   
  Three boxes uses this paradigm:
  - <b> \ref Doc_BoxAlgorithm_2DTopography </b> box: maps the input (which channels are labelled in the 10-20 system standard) to a planar projection of the scalp.
  - <b> \ref Doc_BoxAlgorithm_3DTopography </b> box: maps the input (which channels are labelled in the 10-20 system standard) to a projection on a 3D model of the scalp.
  - <b> \ref Doc_BoxAlgorithm_3DCubes </b> box: an alternative view where each channel is represented by a 3D cube, positionned in space as the electrode would be on the 3D model. 
  The activity is rendered by changing the size and color of the cubes.
  
  <b>Example</b>: Displaying the power of a specific frequency band on a 3D head model.
  \image html 3DTopography_Display.png "Alpha power mapped on a head model using the 3D topography"
  \image latex 3DTopography_Display.png "Alpha power mapped on a head model using the 3D topography" width=10cm

  \subsection Doc_Mensia_AdvViz_Concepts_VisualizationParadigms_Reco The Reconstruction view
  Tomographic reconstruction algorithms offer an inside look, into the brain, from only surface measurements.
  Several techniques exist, including the algorithms of the popular LORETA family which slice the brain in a stack of little cubes called voxels, 
  and computes the <em>inverse model</em>, a model reconstructing the sources of the potentials acquired at the measurement site.
   
  One box implements the source reconstruction view:
  - <b> \ref Doc_BoxAlgorithm_3DSourceVisualization </b> box : displays a 3D source reconstruction using 2394 colored/translucent voxels in a 3D head model. 
  This box expects 2394 input channels, produced by an inverse model (i.e. a spatial filter with N sensor inputs for 2394 sources outputs). This model must be
  tailor-made for the precise EEG setup being used (e.g. using sLORETA).
  
  \image html 3DTomographicVisualization_Display.png "3D tomographic reconstruction using the 3D Tomographic Visualization box"
  \image latex 3DTomographicVisualization_Display.png "3D tomographic reconstruction using the 3D Tomographic Visualization box" width=10cm
  
  \section Doc_Mensia_AdvViz_Concepts_ChannelLocalization Channel localization
  Every visualization box can use the spatial information conveyed by the electrode naming. The channels can be positionned relatively to each 
  other as long as you provide in the box settings a file containing the cartesian coordinates of the electrodes.
  Most of the time, EEG manufacturers use the 10-20 system as an electrode naming standard. For convenience, we provide within the Toolset a file compiling all the
  coordinates of the electrodes in the 10-20 system.
  
  The cartesian coordinates of all the electrodes are computed in the 3D space, where the origin is at the center of [Fpz,Oz] and [T7,T8].
  - the X axis goes from the occipital lobe to the frontal lobe
  - the Y axis goes from the right temporal lobe to the left temporal lobe
  - the Z axis goes from the center of the head to the top
  
  And as for the unit, here are some key points at the maximum of the axis:
  - Fpz (1,0,0)
  - Oz (-1,0,0)
  - T7 (0,1,0)
  - T8 (0,-1,0)
  - Cz (0,0,1)
  
  The following figures illustrates the cartesian coordinates of the extended 10-20 system used in the Mensia Advanced Visualization Toolset.
  \image html CartesianCoordinates1.png "Cartesian coordinates of the 10-20 system, side view."
  \image latex CartesianCoordinates1.png "Cartesian coordinates of the 10-20 system, side view." width=8cm
  \image html CartesianCoordinates2.png "Cartesian coordinates of the 10-20 system, front view."
  \image latex CartesianCoordinates2.png "Cartesian coordinates of the 10-20 system, front view." width=8cm
 
  For more information, please see <i>Oostenveld, R. & Praamstra, P. (2001). The five percent electrode system
  for high-resolution EEG and ERP measurements. Clinical Neurophysiology, 112:713-719</i>
  
  Please note that using the 10-20 system is not mandatory. To use all the Toolset features related to the spatial disposition of the electrodes, you
  just need to provide a file that maps electrode name with their coordinates in the space described above.
  
  The format of this file is simple text. You must provide:
  - the electrode names as a list of quoted labels
  - the coordinate system labels
  - the electrode coordinates of the electrodes, in the same order as in the electrode names
  
  For example:
  \code
[
	["O1" "O2" ... ]
	["x" "y" "z" ]
]
[
	[-0.309017 -0.951057 4.48966e-011 ]
]
[
	[0.309017 -0.951057 4.48966e-011 ]
]
...
  \endcode
  
 For a complete example, please look at the file provided with the Toolset (<i>../share/mensia /openvibe-plugins/cartesian.txt</i>)
 */