FieldTrip

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.toolbox    = 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.toolbox = '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 for
  the whole common time axis defined in the data. It will throw an error
  if the trials are of variable length, or if the time axes of the trials
  are not equal to one another.
 
  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 with the peer-to-peer
  module, this function has the following options:
    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.