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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', 'ar_reverse' (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
cfg.randomseed = 'yes' or a number or vector with the seed value (default = 'yes')
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 a 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
Method 'ar_reverse' implements a multivariate autoregressive
autoregressive model to generate the data, where the model coefficients
are reverse-computed, based on the interaction pattern specified.
Required cfg options:
cfg.coupling = nxn matrix, specifying coupling strength, rows causing
column
cfg.delay = nxn matrix, specifying the delay, in seconds, from one
signal's spectral component to the other signal, rows
causing column
cfg.ampl = nxn matrix, specifying the amplitude
cfg.bpfreq = nxnx2 matrix, specifying the lower and upper frequencies
of the bands that are transmitted, rows causing column
The generated signals will have a spectrum that is 1/f + additional
band-limited components, as specified in the cfg.
See also FT_FREQSIMULATION, FT_DIPOLESIMULATION, FT_SPIKESIMULATION,
FT_CONNECTIVITYANALYSIS
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