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