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FT_MVARANALYSIS performs multivariate autoregressive modeling on
time series data over multiple trials.
Use as
[mvardata] = ft_mvaranalysis(cfg, data)
The input data should be organised in a structure as obtained from
the FT_PREPROCESSING function. The configuration depends on the type
of computation that you want to perform.
The output is a data structure of datatype 'mvar' which contains the
multivariate autoregressive coefficients in the field coeffs, and the
covariance of the residuals in the field noisecov.
The configuration should contain:
cfg.method = the name of the toolbox containing the function for the
actual computation of the ar-coefficients
this can be 'biosig' (default) or 'bsmart'
you should have a copy of the specified toolbox in order
to use mvaranalysis (both can be downloaded directly).
cfg.mvarmethod = scalar (only required when cfg.method = 'biosig').
default is 2, relates to the algorithm used for the
computation of the AR-coefficients by mvar.m
cfg.order = scalar, order of the autoregressive model (default=10)
cfg.channel = 'all' (default) or list of channels for which an mvar model
is fitted. (Do NOT specify if cfg.channelcmb is
defined)
cfg.channelcmb = specify channel combinations as a
two-column cell array with channels in each column between
which a bivariate model will be fit (overrides
cfg.channel)
cfg.keeptrials = 'no' (default) or 'yes' specifies whether the coefficients
are estimated for each trial seperately, or on the
concatenated data
cfg.jackknife = 'no' (default) or 'yes' specifies whether the coefficients
are estimated for all leave-one-out sets of trials
cfg.zscore = 'no' (default) or 'yes' specifies whether the channel data
are z-transformed prior to the model fit. This may be
necessary if the magnitude of the signals is very different
e.g. when fitting a model to combined MEG/EMG data
cfg.demean = 'yes' (default) or 'no' explicit removal of DC-offset
cfg.ems = 'no' (default) or 'yes' explicit removal ensemble mean
ft_mvaranalysis can be used to obtain one set of coefficients across
all time points in the data, also when the trials are of varying length.
ft_mvaranalysis can be also used to obtain time-dependent sets of
coefficients based on a sliding window. In this case the input cfg
should contain:
cfg.t_ftimwin = the width of the sliding window on which the coefficients
are estimated
cfg.toi = [t1 t2 ... tx] the time points at which the windows are
centered
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_PREPROCESSING, FT_SOURCESTATISTICS, FT_FREQSTATISTICS,
FT_TIMELOCKSTATISTICS
```