FieldTrip

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

  FT_CONNECTIVITYSIMULATION simulates channel-level time-series data with a
  specified connectivity structure. This function returns an output data
  structure that resembles the output of FT_PREPROCESSING.
 
  Use as
    [data] = ft_connectivitysimulation(cfg)
 
  where the configuration structure should contain:
    cfg.method      = string, can be 'linear_mix', 'mvnrnd', 'ar' (see below)
    cfg.nsignal     = scalar, number of signals
    cfg.ntrials     = scalar, number of trials
    cfg.triallength = in seconds
    cfg.fsample     = in Hz
 
  Depending on the specific method that is selected, the configuration
  may also contain:
 
  Method 'linear_mix' implements a linear mixing with optional time shifts
  where the number of unobserved signals can be different from the number
  of observed signals
 
  Required cfg options:
    cfg.mix    = matrix, [nsignal x number of unobserved signals]
                 specifying the mixing from the unobserved signals to
                 the observed signals, or
               = matrix, [nsignal x number of unobserved signals x number of
                 samples] specifying the mixing from the
                 unobserved signals to the observed signals which
                 changes as a function of time within the trial
               = cell-arry, [1 x ntrials] with each cell a matrix as
                 specified above, when a trial-specific mixing is
                 required
    cfg.delay  = matrix, [nsignal x number of unobserved signals]
                 specifying the time shift (in samples) between the
                 unobserved signals and the observed signals
 
  Optional cfg options:
    cfg.bpfilter  = 'yes' (or 'no')
    cfg.bpfreq    = [bplow bphigh] (default: [15 25])
    cfg.demean    = 'yes' (or 'no')
    cfg.baselinewindow = [begin end] in seconds, the default is the complete trial
    cfg.absnoise  = scalar (default: 1), specifying the standard
                    deviation of white noise superimposed on top
                    of the simulated signals
 
  Method 'mvnrnd' implements a linear mixing with optional timeshifts in
  where the number of unobserved signals is equal to the number of observed
  signals. This method used the matlab function mvnrnd. The implementation
  is a bit ad-hoc and experimental, so users are discouraged to apply it.
  The time shift occurs only after the linear mixing, so the effect of the
  parameters on the simulation is not really clear. This method will be
  disabled in the future.
 
  Required cfg options:
    cfg.covmat    = covariance matrix between the signals
    cfg.delay     = delay vector between the signals in samples
 
  Optional cfg options:
    cfg.bpfilter  = 'yes' (or 'no')
    cfg.bpfreq    = [bplow bphigh] (default: [15 25])
    cfg.demean    = 'yes' (or 'no')
    cfg.baselinewindow = [begin end] in seconds, the default is the complete trial
    cfg.absnoise  = scalar (default: 1), specifying the standard
                    deviation of white noise superimposed on top
                    of the simulated signals
 
  Method 'ar' implements an multivariate autoregressive model to generate
  the data.
 
  Required cfg options:
    cfg.params   = matrix, [nsignal x nsignal x number of lags] specifying the
                   autoregressive coefficient parameters. A non-zero
                   element at cfg.params(i,j,k) means a
                   directional influence from signal j onto
                   signal i (at lag k).
    cfg.noisecov = matrix, [nsignal x nsignal] specifying the covariance
                   matrix of the innovation process
 
  See also FT_FREQSIMULATION, FT_DIPOLESIMULATION, FT_SPIKESIMULATION,
  FT_CONNECTIVITYANALYSIS