[Eeglablist] Question about the use of ICA and GC

Mon Jul 28 17:00:38 PDT 2014

I think it depends on how we calculate GC and also eliminate the volume conductance problem. Have you looked at the following paper? Also, it would be appreciated if you please cite some papers for your logic/statement:) 

The MVGC multivariate Granger causality toolbox: A new approach to Granger-causal inference Lionel Barnett∗, Anil K. Seth

From: Makoto Miyakoshi [mailto:mmiyakoshi at ucsd.edu] 
Sent: Monday, July 28, 2014 4:43 PM
To: Iman M.Rezazadeh
Cc: Salim Al-wasity; EEGLAB List
Subject: Re: [Eeglablist] Question about the use of ICA and GC

Dear Iman,

Because it can detect spurious connections that should be suppressed.

Makoto

On Mon, Jul 28, 2014 at 3:49 PM, Iman M.Rezazadeh <irezazadeh at ucdavis.edu <mailto:irezazadeh at ucdavis.edu> > wrote:

Hi Makoto, 

Could you please let me know how do you get to this point “By the way if you have more than 3 ICs for GC don't use GC but use RPDC or normalized dDTF” ?

Thanks ! 

Iman

From: eeglablist-bounces at sccn.ucsd.edu <mailto:eeglablist-bounces at sccn.ucsd.edu>  [mailto:eeglablist-bounces at sccn.ucsd.edu <mailto:eeglablist-bounces at sccn.ucsd.edu> ] On Behalf Of Makoto Miyakoshi
Sent: Monday, July 28, 2014 3:30 PM
To: Salim Al-wasity
Cc: eeglablist at sccn.ucsd.edu <mailto:eeglablist at sccn.ucsd.edu> 
Subject: Re: [Eeglablist] Question about the use of ICA and GC

Dear Salim,

> 1- Are the predicted S1(60) and S2(60) independent as the actual ones which obtained using ICA.

If the S1(60) and S2(60) are the predictions made by i.e. AR model from S1(1-59) and S2(1-59) respectively, then they are most likely independent.

> 2- The past values that it used to predict S1(60) and S2(60) are independent. Does this independence affect the predict variables?

Yes, why not?

Makoto

On Sun, Jul 27, 2014 at 8:22 AM, Salim Al-wasity <salim_alwasity at yahoo.com <mailto:salim_alwasity at yahoo.com> > wrote:

Dear Mr. Miyakoshi

Thanks for your reply. Let assumes that an ICA is applied to an observe matrix X (of 2 channels and 100 samples), therefore an S decomposed independent source signal is obtain.

After that GC is used to find the connectivity between S1 and S2 (channel 1 and channel 2 of S), for instance to predict S1(60) and S2(60) based on the past values of S1 and S2 (for a predefined order), then:

1- Are the predicted S1(60) and S2(60) independent as the actual ones which obtained using ICA.

2- The past values that it used to predict S1(60) and S2(60) are independent. Does this independence affect the predict variables?

Sincerely

Salim

On Friday, 25 July 2014, 20:41, Makoto Miyakoshi <mmiyakoshi at ucsd.edu <mailto:mmiyakoshi at ucsd.edu> > wrote:

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 <mailto: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 <tel:%2B44%20742%20371%204444> 

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-- 

Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego

-- 

Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego

-- 

Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego

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