Note that this reference documentation is identical to the help that is displayed in MATLAB when you type “help ft_sourceanalysis”.
FT_SOURCEANALYSIS performs beamformer dipole analysis on EEG or MEG data after preprocessing and a timelocked or frequency analysis Use as either [source] = ft_sourceanalysis(cfg, freq) [source] = ft_sourceanalysis(cfg, timelock) where the data in freq or timelock should be organised in a structure as obtained from the FT_FREQANALYSIS or FT_TIMELOCKANALYSIS 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 FT_PREPARE_SOURCEMODEL or FT_PREPARE_LEADFIELD 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 FT_CHANNELSELECTION 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 FT_PREPARE_HEADMODEL The EEG or MEG sensor positions can be present in the data or can be specified as cfg.elec = structure with electrode positions, see FT_DATATYPE_SENS cfg.grad = structure with gradiometer definition, see FT_DATATYPE_SENS cfg.elecfile = name of file containing the electrode positions, see FT_READ_SENS cfg.gradfile = name of file containing the gradiometer definition, see FT_READ_SENS 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 FT_SOURCEDESCRIPTIVES, FT_SOURCESTATISTICS, FT_PREPARE_LEADFIELD, FT_PREPARE_HEADMODEL, FT_PREPARE_SOURCEMODEL