<div dir="ltr">Dear Bethel,<div><br></div><div>> say A=sunrise and B=ice-cream-sale, then the ICA in EEGLAB should find that A is maximally temporaly independent from B.<br></div><div class="gmail_extra"><br></div><div class="gmail_extra">
ICA would find a correlation between sunrise and ice-cream-sale.</div><div class="gmail_extra"><br></div><div class="gmail_extra">Makoto</div><div class="gmail_extra"><br><div class="gmail_quote">2014-02-10 4:57 GMT-08:00 Bethel Osuagwu <span dir="ltr"><<a href="mailto:b.osuagwu.1@research.gla.ac.uk" target="_blank">b.osuagwu.1@research.gla.ac.uk</a>></span>:<br>
<blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex">Hi<br>
I am not an expert but I just want to give my own opinion!<br>
<br>
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.<br>
<br>
This is what I think, but I wait to be corrected so that I can learn!<br>
<br>
Thanks<br>
Bethel<br>
________________________________________<br>
From: <a href="mailto:eeglablist-bounces@sccn.ucsd.edu">eeglablist-bounces@sccn.ucsd.edu</a> [<a href="mailto:eeglablist-bounces@sccn.ucsd.edu">eeglablist-bounces@sccn.ucsd.edu</a>] On Behalf Of IMALI THANUJA HETTIARACHCHI [<a href="mailto:ith@deakin.edu.au">ith@deakin.edu.au</a>]<br>
Sent: 07 February 2014 01:27<br>
To: <a href="mailto:mullen.tim@gmail.com">mullen.tim@gmail.com</a><br>
Cc: <a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a><br>
Subject: [Eeglablist] Two step source connectivity analysis (as implemented in SIFT)<br>
<div><div class="h5"><br>
Hi Tim and the list,<br>
<br>
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.<br>
<br>
“When using independent component analysis (ICA), we assume the mutual independence<br>
of underlying sources, however when we try to estimate connectivity between EEG sources,<br>
we implicitly assume that the sources may be influenced by each other. This contradicts the<br>
fundamental assumption of mutual independence between sources in ICA [Cheung et al., 2010, Chiang et al., 2012, Haufe et al., 2009 ]. “<br>
<br>
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.<br>
<br>
<br>
· So, how would you support the valid use of SIFT ( ICA+MVAR as a two-step procedure) for the source connectivity analysis?<br>
<br>
<br>
· 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?<br>
<br>
It would be really great to see different thoughts and opinions.<br>
<br>
Kind regards<br>
<br>
Imali<br>
<br>
<br>
Dr. Imali Thanuja Hettiarachchi<br>
Researcher<br>
Centre for Intelligent Systems research<br>
Deakin University, Geelong 3217, Australia.<br>
<br>
Mobile : <a href="tel:%2B61430321972" value="+61430321972">+61430321972</a><br>
</div></div>Email: <a href="mailto:ith@deakin.edu.au">ith@deakin.edu.au</a><mailto:<a href="mailto:ith@deakin.edu.au">ith@deakin.edu.au</a>><br>
Web :<a href="http://www.deakin.edu.au/cisr" target="_blank">www.deakin.edu.au/cisr</a><<a href="http://www.deakin.edu.au/cisr" target="_blank">http://www.deakin.edu.au/cisr</a>><br>
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</blockquote></div><br><br clear="all"><div><br></div>-- <br><div dir="ltr">Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<br></div>
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