[Eeglablist] Question about the use of ICA and GC

Makoto Miyakoshi mmiyakoshi at ucsd.edu
Mon Jul 28 16:42:43 PDT 2014

```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>
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] *On Behalf Of *Makoto Miyakoshi
> *Sent:* Monday, July 28, 2014 3:30 PM
> *To:* Salim Al-wasity
> *Cc:* 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>
> 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>
> 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>
> 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
>
>
>
>
>
>
>
> --
>
> 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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