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

```  FT_CONNECTIVITY_GRANGER computes spectrally resolved granger causality. This
implementation is loosely based on the code used in Brovelli, et. al., PNAS 101,
9849-9854 (2004).

Use as
[GRANGER, V, N] = FT_CONNECTIVITY_GRANGER(H, Z, S, ...)

The input data should be
H = spectral transfer matrix, Nrpt x Nchan x Nchan x Nfreq (x Ntime),
or Nrpt x Nchancmb x Nfreq (x Ntime). Nrpt can be 1.
Z = the covariance matrix of the noise, Nrpt x Nchan x Nchan (x Ntime),
or Nrpt x Nchancmb (x Ntime).
S = the cross-spectral density matrix with the same dimensionality as H.

Additional optional input arguments come as key-value pairs:
'dimord'  = required string specifying how to interpret the input data
supported values are 'rpt_chan_chan_freq(_time) and
'rpt_chan_freq(_time), 'rpt_pos_pos_XXX' and 'rpt_pos_XXX'
'method'  = 'granger' (default), or 'instantaneous', or 'total'.
'hasjack' = 0 (default) is a boolean specifying whether the input
contains leave-one-outs, required for correct variance
estimate
'powindx' = is a variable determining the exact computation, see below

If the inputdata is such that the channel-pairs are linearly indexed, granger
causality is computed per quadruplet of consecutive entries, where the convention
is as follows:

H(:, (k-1)*4 + 1, :, :, :) -> 'chan1-chan1'
H(:, (k-1)*4 + 2, :, :, :) -> 'chan1->chan2'
H(:, (k-1)*4 + 3, :, :, :) -> 'chan2->chan1'
H(:, (k-1)*4 + 4, :, :, :) -> 'chan2->chan2'

The same holds for the Z and S matrices.

Pairwise block-granger causality can be computed when the inputdata has
dimensionality Nchan x Nchan. In that case powindx should be specified, as a 1x2
cell-array indexing the individual channels that go into each 'block'.