[FieldTrip] Granger analysis

[Schoffelen, J.M. (Jan Mathijs)]

Hi Matt,

I have never done this myself, but I think that in principle it should be possible to fit a set of models, and then compute the residuals’ variance ratio by hand.
In other words, assuming data1 and data2 to be variables that consist of a single channel:

mvar1 = ft_mvaranalysis(cfg, data1);
mvar2 = ft_mvaranalysis(cfg, data2);
mvar12 = ft_mvaranalysis(cfg, ft_appenddata([],data1,data2));

GC1to2 = log(mvar2.noisecov(1,1)./mvar12.noisecov(2,2));
GC2to1 = log(mvar1.noisecov(1,1)./mvar12.noisecov(1,1));

Best wishes,
Jan-Mathijs

J.M.Schoffelen, MD PhD
Senior Researcher, VIDI-fellow - PI, language in interaction
Telephone: +31-24-3614793
Physical location: room 00.028
Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands



On 12 Aug 2017, at 00:05, MATTHEW I BANKS <mibanks at wisc.edu<mailto:mibanks at wisc.edu>> wrote:

Greetings. What is the easiest way to compute non-spectrally resolved Granger causality, i.e. log(var(e_x)/var(e_xy)), where e_x and e_xy are the AR model error terms?

-Matt

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