[Eeglablist] Two step source connectivity analysis (as implemented in SIFT)

[Makoto Miyakoshi]

Dear Bethel,

> say A=sunrise and B=ice-cream-sale, then the ICA in EEGLAB should find
that A is maximally  temporaly independent from B.

ICA would find a correlation between sunrise and ice-cream-sale.

Makoto

2014-02-10 4:57 GMT-08:00 Bethel Osuagwu <b.osuagwu.1 at research.gla.ac.uk>:

> Hi
> I am not an expert but I just want to give my own opinion!
>
> I do not think that temporal independence of two variables (A and B)
> violets causality between them as implemented in SIFT. In fact if  say
> A=sunrise and B=ice-cream-sale, then the ICA in EEGLAB should find that A
> is maximally  temporaly independent from B. However we know there is causal
> flow from A to B.
>
> This is what I think, but I wait to be corrected so that I can learn!
>
> Thanks
> Bethel
> ________________________________________
> From: eeglablist-bounces at sccn.ucsd.edu [eeglablist-bounces at sccn.ucsd.edu]
> On Behalf Of IMALI THANUJA HETTIARACHCHI [ith at deakin.edu.au]
> Sent: 07 February 2014 01:27
> To: mullen.tim at gmail.com
> Cc: eeglablist at sccn.ucsd.edu
> Subject: [Eeglablist] Two step source connectivity analysis (as
> implemented     in SIFT)
>
> Hi Tim and the list,
>
> I am just in need of a clarification regarding the ICA source
> reconstruction and the subsequent MVAR -based effective connectivity
> analysis using the components, which is the basis of the SIFT toolbox. I
> was trying to use this approach in my work but was questioned on the
> validity using ICA and subsequent MVAR analysis by my colleagues.
>
> "When using independent component analysis (ICA), we assume the mutual
> independence
> of underlying sources, however when we try to estimate connectivity
> between EEG sources,
> we implicitly assume that the sources may be  influenced by each other.
> This contradicts the
> fundamental assumption of mutual independence between sources in ICA
> [Cheung et al., 2010, Chiang et al., 2012, Haufe et al., 2009 ]. "
>
> So due to this reason different approaches such as MVARICA,
> CICAAR(convolution ICA+MVAR),  SCSA and state space-based methods have been
> proposed as ICA+MVAR based source connectivity analysis techniques.
>
>
> ·         So, how would you support the valid use of SIFT ( ICA+MVAR as a
> two-step procedure) for the source connectivity analysis?
>
>
> ·         If I argue that I do not assume independent sources but rely on
> the fact that ICA will decompose the EEG signals and output 'maximally
> independent' sources and then, I subsequently model for the dependency,
> will you agree with me? How valid would my argument be?
>
> It would be really great to see different thoughts and opinions.
>
> Kind regards
>
> Imali
>
>
> Dr. Imali Thanuja Hettiarachchi
> Researcher
> Centre for Intelligent Systems research
> Deakin University, Geelong 3217, Australia.
>
> Mobile : +61430321972
> Email: ith at deakin.edu.au<mailto:ith at deakin.edu.au>
> Web :www.deakin.edu.au/cisr<http://www.deakin.edu.au/cisr>
>
> [cid:image001.jpg at 01CF23FF.F8259940]
>
>
>
>
>
>
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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