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

  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