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

  FT_STRATIFY tries to reduce the variance in a specific feature in the data
  that is not related to an effect in two or multiple conditions, but where
  that feature may confound the analysis. Stratification is implemented by
  randomly removing elements from the data, making the distribution of the
  data equal on that feature.
 
  Use as
    [output]          = ft_stratify(cfg, input1, input2, ...), or
    [output, binaxis] = ft_stratify(cfg, input1, input2, ...)
 
  For the histogram and the split method, each input is a Nchan X Nobs
  matrix. The output is a cell-array with in each cell the same data as in
  the corresponding input, except that the observations that should be
  removed are marked with a NaN.
 
  For the equatespike method, each input is a Ntrials X 1 cell-array. Each
  trial should contain the spike firing moments (i.e. a logical Nchans X
  Nsamples matrix). The output is a cell-array with in each cell the same
  data as in the corresponding input, except that spike numbers have been
  equated in each trial and channel.
 
  The configuration should contain
    cfg.method      = 'histogram'
                      'splithilo'
                      'splitlohi'
                      'splitlolo'
                      'splithihi'
                      'equatespike'
 
  The following options apply only to histogram and split methods.
    cfg.equalbinavg = 'yes'
    cfg.numbin      = 10
    cfg.numiter     = 2000
 
  The following options apply only to the equatespike method.
    cfg.pairtrials  = 'spikesort', 'linkage' or 'no' (default = 'spikesort')
    cfg.channel     = 'all' or list with indices ( default = 'all')
 
  See also FT_FREQSTATISTICS, FT_TIMELOCKSTATISTICS, FT_SOURCESTATISTICS