[Eeglablist] Question about the use of ICA and GC

[Makoto Miyakoshi]

Dear Salim,

ICA assures *instantaneous* independence, while GC calculates *temporal*
causality. This means that ICA does not know what happens in the next
moment, but GC does.

By the way if you have more than 3 ICs for GC don't use GC but use RPDC or
normalized dDTF.

Makoto


On Fri, Jul 25, 2014 at 4:41 AM, Salim Al-wasity <salim_alwasity at yahoo.com>
wrote:

> Dears
> Have a nice day. Kindly I have a question regarding ICA:
> I have EEG data of (44 channels X 294000 samples)
>
> 1- I applied the ICA to separate the noise and find the ICs which are
> belong to brain activities.
> 2- I used the Granger Causality (GC) in SIFT to find the connectivity
> between these ICs and discover which component influence which. However I
> am not sure about the results that I have got.
>
> My question is that If I used ICA to decompose the EEG signal into their
> sources (hint: the decomposed ICs is much less than the actual brain
> sources), theoretically these ICs would be independent, and the use of GC
> would be useless since the latter algorithm search for the dependence
> across ICs?
> Or the ICA will minimize the mutual information, and the separated
> components will not be ~100% independent, therefore each component has more
> that one source, and GC  can find some influence across these components
> for the remaining not separated sources.
>
> Your cooperation is highly appreciated
>
> Yours
> Salim Al-Wasity
> PhD student
> Rehabilitation Centre
> Biomedical Engineering Department-School of Engineering
> University of Glasgow
> Glasgow-United Kingdom
> +44 742 371 4444
>
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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