[Eeglablist] Granger causality for resting state EEG data

[Daniele Marinazzo]

Of course Granger causality can be applied to resting EEG data. Then some
people think it's completely pointless to analyze connectivity at the
sensor (scalp) level. Other will say that it's anyway a useful tool to
detect changes in states, provided that you are careful with the
speculations of what could possibly happen inside the brain.

With resting state recordings it's more difficult to do an efficient source
reconstruction, although it can be done.

Another thing is that EEG signal is pretty nonlinear, and the standard
formulation of GC is limited to the linear case.
I have developed a tool to detect nonlinear Granger causal interactions,
you can find the code here
http://users.ugent.be/~dmarinaz/KGC.html

Or you can consider another tool such as Transfer Entropy
http://users.ugent.be/~dmarinaz/MuTE.html
http://www.trentool.de/

For measures in the frequency domain (such as PDC, DTF) you can take a look
at this toolbox
http://www.science.unitn.it/~nollo/research/sigpro/eMVAR.html

or Granger Causality in the frequency domain
http://www.sussex.ac.uk/sackler/mvgc/

But keep in mind that these measures are linear, while EEG signals are not.

or Phase Slope Index
http://doc.ml.tu-berlin.de/causality/

best regards

On Thu, Feb 5, 2015 at 11:51 AM, Eric HG <erichg2013 at gmail.com> wrote:

> Hi everybody,
>
> Does anyone know whether Granger causality is widely used for resting
> state EEG data? Or do people use partial directed coherence instead?
>
> And is there any toolbox with codes for these types of analysis?
>
> Best regards,
>
> Eric
>
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