FieldTrip

Note that this reference documentation is identical to the help that is displayed in Matlab when you type “help ft_dipolefitting”.

  FT_DIPOLEFITTING perform grid search and non-linear fit with one or multiple
  dipoles and try to find the location where the dipole model is best able
  to explain the measured EEG or MEG topography.
 
  This function will initially scan the whole brain with a single dipole on
  a regular coarse grid, and subsequently start at the most optimal location
  with a non-linear search. Alternatively you can specify the initial
  location of the dipole(s) and the non-linear search will start from there.
 
  Use as
    [source] = ft_dipolefitting(cfg, data)
 
  The configuration has the following general fields
    cfg.numdipoles  = number, default is 1
    cfg.symmetry    = 'x', 'y' or 'z' symmetry for two dipoles, can be empty (default = [])
    cfg.channel     = Nx1 cell-array with selection of channels (default = 'all'),
                      see FT_CHANNELSELECTION for details
    cfg.gridsearch  = 'yes' or 'no', perform global search for initial
                      guess for the dipole parameters (default = 'yes')
    cfg.nonlinear   = 'yes' or 'no', perform nonlinear search for optimal
                      dipole parameters (default = 'yes')
 
  You should specify the volume conductor model, see FT_FETCH_VOL, and the 
  sensor information, see FT_FETCH_SENS.
 
  If you start with a grid search, you should specify the grid locations at
  which a test dipole will be placed. 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
  Alternatively a complete grid with dipole positions and precomputed
  leadfields can be specified
    cfg.grid            = structure, see FT_PREPARE_LEADFIELD
  or 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        = Nx3 matrix with position of each source
    cfg.grid.dim        = [Nx Ny Nz] vector with dimensions in case of 3-D grid (optional)
    cfg.grid.inside     = vector with indices of the sources inside the brain (optional)
    cfg.grid.outside    = vector with indices of the sources outside the brain (optional)
 
  If you do not start with a grid search, you have to give a starting location
  for the nonlinear search
    cfg.dip.pos     = initial dipole position, matrix of Ndipoles x 3
 
  The conventional approach is to fit dipoles to event-related averages, which
  within fieldtrip can be obtained from the FT_TIMELOCKANALYSIS or from
  the FT_TIMELOCKGRANDAVERAGE function. This has the additional options
    cfg.latency     = [begin end] in seconds or 'all' (default = 'all')
    cfg.model       = 'moving' or 'regional'
  A moving dipole model has a different position (and orientation) for each
  timepoint, or for each component. A regional dipole model has the same
  position for each timepoint or component, and a different orientation.
 
  You can also fit dipoles to the spatial topographies of an independent
  component analysis, obtained from the FT_COMPONENTANALYSIS function.
  This has the additional options
    cfg.component   = array with numbers (can be empty -> all)
 
  You can also fit dipoles to the spatial topographies that are present
  in the data in the frequency domain, which can be obtained using the
  FT_FREQANALYSIS function. This has the additional options
    cfg.frequency   = single number (in Hz)
 
  Low level details of the fitting can be specified in the cfg.dipfit structure
    cfg.dipfit.display  = level of display, can be 'off', 'iter', 'notify' or 'final' (default = 'iter')
    cfg.dipfit.optimfun = function to use, can be 'fminsearch' or 'fminunc' (default is determined automatic)
    cfg.dipfit.maxiter  = maximum number of function evaluations allowed (default depends on the optimfun)
 
  To facilitate data-handling and distributed computing with the peer-to-peer
  module, this function has the following options:
    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_SOURCEANALYSIS, FT_PREPARE_LEADFIELD, FT_FETCH_SENS