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--- reference:ft_sourceanalysis [2018/08/23 14:43]
+++ reference:ft_sourceanalysis [2018/08/23 14:43] (current)
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+=====  FT_SOURCEANALYSIS =====
+Note that this reference documentation is identical to the help that is displayed in MATLAB when you type "help ft_sourceanalysis".
+<html><pre>
+  <a href=/reference/ft_sourceanalysis><font color=green>FT_SOURCEANALYSIS</font></a> performs beamformer dipole analysis on EEG or MEG data
+  after preprocessing and a timelocked or frequency analysis
+  Use as
+    [source] = ft_sourceanalysis(cfg, freq)
+  or
+    [source] = ft_sourceanalysis(cfg, timelock)
+  where the second input argument with the data should be organised in a structure
+  as obtained from the <a href=/reference/ft_freqanalysis><font color=green>FT_FREQANALYSIS</font></a> or <a href=/reference/ft_timelockanalysis><font color=green>FT_TIMELOCKANALYSIS</font></a> function. The
+  configuration "cfg" is a structure containing information about source positions
+  and other options.
+  The different source reconstruction algorithms that are implemented are
+    cfg.method     = 'lcmv'    linear constrained minimum variance beamformer
+                     'sam'     synthetic aperture magnetometry
+                     'dics'    dynamic imaging of coherent sources
+                     'pcc'     partial cannonical correlation/coherence
+                     'mne'     minimum norm estimation
+                     'rv'      scan residual variance with single dipole
+                     'music'   multiple signal classification
+                     'sloreta' standardized low-resolution electromagnetic tomography
+                     'eloreta' exact low-resolution electromagnetic tomography
+  The DICS and PCC methods are for frequency or time-frequency domain data, all other
+  methods are for time domain data. ELORETA can be used both for time, frequency and
+  time-frequency domain data.
+  The source model to use in the reconstruction should be specified as
+    cfg.grid            = structure, see <a href=/reference/ft_prepare_sourcemodel><font color=green>FT_PREPARE_SOURCEMODEL</font></a> or <a href=/reference/ft_prepare_leadfield><font color=green>FT_PREPARE_LEADFIELD</font></a>
+  The positions of the dipoles can be specified as a regular 3-D
+  grid that is aligned with the axes of the head coordinate system
+    cfg.grid.xgrid      = vector (e.g. -20:1:20) or 'auto' (default = 'auto')
+    cfg.grid.ygrid      = vector (e.g. -20:1:20) or 'auto' (default = 'auto')
+    cfg.grid.zgrid      = vector (e.g.   0:1:20) or 'auto' (default = 'auto')
+    cfg.grid.resolution = number (e.g. 1 cm) for automatic grid generation
+    cfg.grid.inside     = N*1 vector with boolean value whether grid point is inside brain (optional)
+    cfg.grid.dim        = [Nx Ny Nz] vector with dimensions in case of 3-D grid (optional)
+  If the source model destribes a triangulated cortical sheet, it is described as
+    cfg.grid.pos        = N*3 matrix with the vertex positions of the cortical sheet
+    cfg.grid.tri        = M*3 matrix that describes the triangles connecting the vertices
+  Alternatively the position of a few dipoles at locations of interest can be
+  specified, for example obtained from an anatomical or functional MRI
+    cfg.grid.pos        = N*3 matrix with position of each source
+  Besides the source positions, you may also include previously computed
+  spatial filters and/or leadfields like this
+    cfg.grid.filter
+    cfg.grid.leadfield
+  The following strategies are supported to obtain statistics for the source parameters using
+  multiple trials in the data, either directly or through a resampling-based approach
+    cfg.rawtrial      = 'no' or 'yes'   construct filter from single trials, apply to single trials. Note that you also may want to set cfg.keeptrials='yes' to keep all trial information, especially if using in combination with grid.filter
+    cfg.jackknife     = 'no' or 'yes'   jackknife resampling of trials
+    cfg.pseudovalue   = 'no' or 'yes'   pseudovalue resampling of trials
+    cfg.bootstrap     = 'no' or 'yes'   bootstrap resampling of trials
+    cfg.numbootstrap  = number of bootstrap replications (e.g. number of original trials)
+  If none of these options is specified, the average over the trials will
+  be computed prior to computing the source reconstruction.
+  To obtain statistics over the source parameters between two conditions, you
+  can also use a resampling procedure that reshuffles the trials over both
+  conditions. In that case, you should call the function with two datasets
+  containing single trial data like
+    [source] = ft_sourceanalysis(cfg, freqA, freqB)
+    [source] = ft_sourceanalysis(cfg, timelockA, timelockB)
+  and you should specify
+    cfg.randomization      = 'no' or 'yes'
+    cfg.permutation        = 'no' or 'yes'
+    cfg.numrandomization   = number, e.g. 500
+    cfg.numpermutation     = number, e.g. 500 or 'all'
+  If you have not specified a grid with pre-computed leadfields,
+  the leadfield for each grid location will be computed on the fly.
+  In that case you can modify the leadfields by reducing the rank
+  (i.e.  remove the weakest orientation), or by normalizing each
+  column.
+    cfg.reducerank  = 'no', or number (default = 3 for EEG, 2 for MEG)
+    cfg.normalize   = 'no' or 'yes' (default = 'no')
+  Other configuration options are
+    cfg.channel       = Nx1 cell-array with selection of channels (default = 'all'),
+                        see <a href=/reference/ft_channelselection><font color=green>FT_CHANNELSELECTION</font></a> for details
+    cfg.frequency     = single number (in Hz)
+    cfg.latency       = single number in seconds, for time-frequency analysis
+    cfg.lambda        = number or empty for automatic default
+    cfg.refchan       = reference channel label (for coherence)
+    cfg.refdip        = reference dipole location (for coherence)
+    cfg.supchan       = suppressed channel label(s)
+    cfg.supdip        = suppressed dipole location(s)
+    cfg.keeptrials    = 'no' or 'yes'
+    cfg.keepleadfield = 'no' or 'yes'
+    cfg.projectnoise  = 'no' or 'yes'
+    cfg.keepfilter    = 'no' or 'yes'
+    cfg.keepcsd       = 'no' or 'yes'
+    cfg.keepmom       = 'no' or 'yes'
+    cfg.feedback      = 'no', 'text', 'textbar', 'gui' (default = 'text')
+  The volume conduction model of the head should be specified as
+    cfg.headmodel     = structure with volume conduction model, see <a href=/reference/ft_prepare_headmodel><font color=green>FT_PREPARE_HEADMODEL</font></a>
+  The EEG or MEG sensor positions can be present in the data or can be specified as
+    cfg.elec          = structure with electrode positions, see <a href=/reference/ft_datatype_sens><font color=green>FT_DATATYPE_SENS</font></a>
+    cfg.grad          = structure with gradiometer definition, see <a href=/reference/ft_datatype_sens><font color=green>FT_DATATYPE_SENS</font></a>
+    cfg.elecfile      = name of file containing the electrode positions, see <a href=/reference/ft_read_sens><font color=green>FT_READ_SENS</font></a>
+    cfg.gradfile      = name of file containing the gradiometer definition, see <a href=/reference/ft_read_sens><font color=green>FT_READ_SENS</font></a>
+  To facilitate data-handling and distributed computing you can use
+    cfg.inputfile   =  ...
+    cfg.outputfile  =  ...
+  If you specify one of these (or both) the input data will be read from a *.mat
+  file on disk and/or the output data will be written to a *.mat file. These mat
+  files should contain only a single variable, corresponding with the
+  input/output structure.
+  See also <a href=/reference/ft_sourcedescriptives><font color=green>FT_SOURCEDESCRIPTIVES</font></a>, <a href=/reference/ft_sourcestatistics><font color=green>FT_SOURCESTATISTICS</font></a>, <a href=/reference/ft_prepare_leadfield><font color=green>FT_PREPARE_LEADFIELD</font></a>,
+  <a href=/reference/ft_prepare_headmodel><font color=green>FT_PREPARE_HEADMODEL</font></a>, <a href=/reference/ft_prepare_sourcemodel><font color=green>FT_PREPARE_SOURCEMODEL</font></a>
+</pre></html>