<div dir="ltr">Dear John,<div><br></div><div>> We can expect that the PCA destroy the linear relation between the variables <br></div><div><br></div><div>No, I think the higher-order correlation is still there.</div><div>
PCA achieves uncorrelation, while ICA achieves independence. The difference between the two is higher-order correlation, if I understand it correctly.</div><div><br></div><div>Makoto</div><div class="gmail_extra"><br><div class="gmail_quote">
2014-05-17 7:46 GMT-07:00 jfochoaster . <span dir="ltr"><<a href="mailto:jfochoaster@gmail.com" target="_blank">jfochoaster@gmail.com</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">
<div dir="ltr"><div><div><div>Dear Makoto, what about PCA? We can expect that the PCA destroy the linear relation between the variables implying that the MVAR model become impossible to find?<br><br></div>I do this question because many ICA approaches uses PCA as a previous step<br>
<br></div>Best"<br><br></div>JOhn Ochoa<br></div><div class="gmail_extra"><div><div class="h5"><br><br><div class="gmail_quote">On Thu, May 8, 2014 at 11:54 AM, Makoto Miyakoshi <span dir="ltr"><<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>></span> wrote:<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"><div dir="ltr">Dear Iman,<div><br></div><div>I like that analogy.</div>
<div><br></div><div>My usual anthropomorphism is that an ICA has an eye that captures only 1 time frame and does not know extension in time.</div>
<div>
<br></div><div>Makoto</div></div><div class="gmail_extra"><br><br><div class="gmail_quote">2014-04-30 16:58 GMT-07:00 Iman M.Rezazadeh <span dir="ltr"><<a href="mailto:irezazadeh@ucdavis.edu" target="_blank">irezazadeh@ucdavis.edu</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"><div bgcolor="white" link="blue" vlink="purple" lang="EN-US">
<div><p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)">Hi all , <u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)">I just want to add more to the following comments from me and other folks. I have been thinking about independent sources and causality btw them !!! and coming up with the following example. Hope it is helpful :<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)"><u></u> <u></u></span></p><p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)">Suppose that John and Mary and other people are in a party and John asked Mary “How are you?” and Mary replied “I am fine, yourself?”. A listener from a distance could hardly hear the communication because of a lot of noises from people in the environment. If ICA is applied to the sounds properly then it is possible to separate John’s, Mary’s and other people’s voices. Now, there are three independent (separate) sources but Mary’s dialog is correlated with John’s dialog or more precisely John’s dialog is a causal factor of Mary’s dialog. Thus, being independent does not imply being uncorrelated or non-causal.<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)"><u></u> <u></u></span></p><p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)">Best<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)">Iman<u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)">============================================<u></u><u></u></span></p><p class="MsoNormal"><b><span style="font-size:10pt;font-family:'Arial Rounded MT Bold',sans-serif;color:rgb(112,48,160)">Iman M.Rezazadeh, Ph.D. , M.Sc., B.Sc.<u></u><u></u></span></b></p>
<p class="MsoNormal"><span style="font-size:10pt;font-family:Arial,sans-serif;color:rgb(31,73,125)">UCLA David Geffen School of Medicine</span><span style="color:rgb(31,73,125)"><u></u><u></u></span></p><p class="MsoNormal">
<span style="font-size:10pt;font-family:Arial,sans-serif;color:rgb(31,73,125)">Semel Institute for Neuroscience and Human Behavior</span><span style="color:rgb(31,73,125)"><u></u><u></u></span></p><p class="MsoNormal">
<span style="font-size:10pt;font-family:Arial,sans-serif;color:rgb(31,73,125)">760 Westwood Plaza, Ste 47-448</span><span style="color:rgb(31,73,125)"><u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:10pt;font-family:Arial,sans-serif;color:rgb(31,73,125)">Los Angeles, CA 90095<u></u><u></u></span></p>
<p class="MsoNormal"><span style="color:rgb(31,73,125)">Join me on LinkedIn at : <a href="http://www.linkedin.com/pub/iman-m-rezazadeh/10/859/840/" target="_blank"><span style="color:rgb(5,99,193)">http://www.linkedin.com/pub/iman-m-rezazadeh/10/859/840/</span></a><u></u><u></u></span></p>
<p class="MsoNormal"><span style="color:rgb(31,73,125)"><u></u> <u></u></span></p><p class="MsoNormal"><a name="1460aa5a85f9faea_145dcc300046d6da_145c03be4f539de3__MailEndCompose"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)"><u></u> <u></u></span></a></p>
<div><div style="border-style:solid none none;border-top-color:rgb(225,225,225);border-top-width:1pt;padding:3pt 0in 0in"><p class="MsoNormal"><b><span style="font-size:11pt;font-family:Calibri,sans-serif;color:windowtext">From:</span></b><span style="font-size:11pt;font-family:Calibri,sans-serif;color:windowtext"> <a href="mailto:eeglablist-bounces@sccn.ucsd.edu" target="_blank">eeglablist-bounces@sccn.ucsd.edu</a> [mailto:<a href="mailto:eeglablist-bounces@sccn.ucsd.edu" target="_blank">eeglablist-bounces@sccn.ucsd.edu</a>] <b>On Behalf Of </b>Iman M.Rezazadeh<br>
<b>Sent:</b> Wednesday, February 19, 2014 2:34 PM<br><b>To:</b> 'Andrei Medvedev'; <a href="mailto:eeglablist@sccn.ucsd.edu" target="_blank">eeglablist@sccn.ucsd.edu</a><br><b>Subject:</b> Re: [Eeglablist] Two step source connectivity analysis (as implemented in SIFT)<u></u><u></u></span></p>
</div></div><p class="MsoNormal"><u></u> <u></u></p><p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)">Thanks Andrei for elaborating this in more details. Also in my former post,<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)">I forgot to mentioned the imaginary coherence method as suggested on Nolte et al. work and I agree with you on this as well. <u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)">-Iman<u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)"><u></u> <u></u></span></p>
<div><div style="border-style:solid none none;border-top-color:rgb(225,225,225);border-top-width:1pt;padding:3pt 0in 0in"><p class="MsoNormal"><b><span style="font-size:11pt;font-family:Calibri,sans-serif;color:windowtext">From:</span></b><span style="font-size:11pt;font-family:Calibri,sans-serif;color:windowtext"> <a href="mailto:eeglablist-bounces@sccn.ucsd.edu" target="_blank">eeglablist-bounces@sccn.ucsd.edu</a> [<a href="mailto:eeglablist-bounces@sccn.ucsd.edu" target="_blank">mailto:eeglablist-bounces@sccn.ucsd.edu</a>] <b>On Behalf Of </b>Andrei Medvedev<br>
<b>Sent:</b> Wednesday, February 19, 2014 12:18 PM<br><b>To:</b> <a href="mailto:eeglablist@sccn.ucsd.edu" target="_blank">eeglablist@sccn.ucsd.edu</a><br><b>Subject:</b> Re: [Eeglablist] Two step source connectivity analysis (as implemented in SIFT)<u></u><u></u></span></p>
</div></div><p class="MsoNormal"><u></u> <u></u></p><p class="MsoNormal" style="margin-bottom:12pt">Hi All,<br><br>I believe Iman gave an important point for the discussion. Let me reiterate it. Causality (Granger or any other causality algorithm for that matter) implies that there is a TIME DELAY between the first signal (the source of influence) and the second signal (the recipient of influence). While, on the other hand, ICA is essentially tries to eliminate INSTANTANEOUS dependence between signals i.e, at each CURRENT time point. Therefore, causality and ICA do not contradict (at least, conceptually). Any source reconstruction algorithm is also conceptually similar to ICA b/c it minimizes this instantaneous dependence between signals. The most important issue here is that this way we minimize a possible artefactual component present in both signals such as 'influence' simply due to volume conductance. It makes sense b/c (usually) 'real' influence is NOT instantaneous and takes some time to occur (but see below for the important exception).<br>
<br>So, if one does ICA and then calculates Granger (or any other type of autoregressive (AR) modeling) between components x(t) and y(t), the expected (and ideal) result would be that the influence between x(t) and y(t) should be close to zero (thanks to ICA) but there may be a non-zero influence at time shifts >0 (at t and t-1 etc). All seems to be fine (I am putting aside the fact that 'no algorithm is perfect' and small delays may still result in some amount of instantaneous correlation b/c signals may not be perfect Poisson processes and thus have some 'memory' i.e., their autocorrelation functions are not delta-functions).<br>
<br>This approach is similar to the imaginary coherence which is insensitive to instantaneous effects of volume conductance (Nolte et al 2004). <br><br>But to add more to the discussion, this approach means that when we minimize instantaneous effects, we may overlook a real 'zero-delay' interaction when two signals are synchronized at phase delay =0. The good example of such zero-delay interaction is gamma-band synchrony. Here, the zero-phase is achieved through the emergent property of the network itself (due to mutual inhibitory and excitatory connections). To reveal this zero-delay interaction in the presence of volume conductance seems to be a hard problem. But I would still argue in favor of removal instantaneous effects simply because they are huge in scalp EEG. Also, 'physiological'/'real' zero-phase synchrony is likely to be 'not perfect' giving rise to small deviations from zero from time to time, which would then be 'detected' by Granger/AR/imag coh).<br>
<br>I also agree that going to the source space instead of the channel space (through ICA or other source reconstruction algorithms) is not free of its own limitations. Perhaps, applying Granger/AR (with 'instantaneous' coefficients ignored) or imaginary coh to the channel data could be a method of choice as well.<br>
<br>Best,<br>Andrei Medvedev<u></u><u></u></p><pre>-- <u></u><u></u></pre><pre>Andrei Medvedev, PhD<u></u><u></u></pre><pre>Assistant Professor,<u></u><u></u></pre><pre>Center for Functional and Molecular Imaging<u></u><u></u></pre>
<pre>Georgetown University<u></u><u></u></pre><pre>4000 Reservoir Rd, NW<u></u><u></u></pre><pre>Washington DC, 20057<u></u><u></u></pre><p class="MsoNormal"><br>On 2/19/2014 1:18 PM, Makoto Miyakoshi wrote: <u></u><u></u></p>
<blockquote style="margin-top:5pt;margin-bottom:5pt"><div><p class="MsoNormal">Dear Iman and all, <u></u><u></u></p><div><p class="MsoNormal"><u></u> <u></u></p></div><div><p class="MsoNormal">So are you saying independent sources can Granger cause each other?<u></u><u></u></p>
</div><div><p class="MsoNormal"><u></u> <u></u></p></div><div><p class="MsoNormal">I agree with Joe and you. I'm not a specialist, but I would imagine (correct me if I'm wrong) that ICs are <i>usually</i> independent <i>except</i> when they are perturbed event-relatedly. In such moments independence are transiently lost and ICs start to Granger cause each other... I tend to think in this way because stationarity depends on time scale. So in the sense it's correct to say ICs are <i>not always</i> independent, because its independency changes from timepoint to timepoint. You can see this visualization with one of AMICA tools. However I haven't seen a log likelihood drop around the event, which contradicts my explanation above, so I could be wrong somewhere. Multiple model AMICA does extract peri-event-onset periods as a different model though.<u></u><u></u></p>
</div><div><p class="MsoNormal"><u></u> <u></u></p></div><div><p class="MsoNormal">Note also that there is an issue of IC subspace within which ICs are always intra-dependent.<u></u><u></u></p></div><div><p class="MsoNormal">
<u></u> <u></u></p></div><div><p class="MsoNormal">Makoto <u></u><u></u></p></div></div><div><p class="MsoNormal" style="margin-bottom:12pt"><u></u> <u></u></p><div><p class="MsoNormal">2014-02-19 0:53 GMT-08:00 Iman M.Rezazadeh <<a href="mailto:irezazadeh@ucdavis.edu" target="_blank">irezazadeh@ucdavis.edu</a>>:<u></u><u></u></p>
<blockquote style="border-style:none none none solid;border-left-color:rgb(204,204,204);border-left-width:1pt;padding:0in 0in 0in 6pt;margin:5pt 0in 5pt 4.8pt"><div><div><p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)">I would like step in and add more comments which may be helpful (hopefully):</span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)"> </span><u></u><u></u></p><p class="MsoNormal"><a name="1460aa5a85f9faea_145dcc300046d6da_145c03be4f539de3_144495a6d99af755__MailEndCompose"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)">The assumption of ICA is : The observed data is the sum of a set of inputs which have been mixed together in an unknown fashion and the aim of ICA is to discover both the inputs and how they were mixed. So, after ICA we have some sources which are temporally independent. In other words, they are independent at time t McKeown, et al. (1998)</span></a><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)"> </span><u></u><u></u></p><p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)">However and based on Clive Granger talk at 2003 Nobel Laureate in Economics “The basic "Granger Causality" definition is quite simple. Suppose that we have three terms, X<sub>t</sub>, Y<sub>t</sub>, and W<sub>t</sub>, and that we first attempt to forecast X<sub>t+1</sub> using past terms of Y<sub>t</sub> and W<sub>t</sub>. We then try to forecast X<sub>t+1</sub> using past terms of X<sub>t</sub>, Y<sub>t</sub>, and W<sub>t</sub>. If the second forecast is found to be more successful, according to standard cost functions, then the past of Y appears to contain information helping in forecasting X<sub>t+1</sub> that is not in past X<sub>t</sub> or W<sub>t. </sub>… Thus, Y<sub>t</sub> would "Granger cause" X<sub>t+1</sub> if (a) Y<sub>t</sub> occurs before X<sub>t+1</sub> ; and (b) it contains information useful in forecasting X<sub>t+1</sub> that is not found in a group of other appropriate variables.” So, in Granger causality we try to relate time t+1 to t.</span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)"> </span><u></u><u></u></p><p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)">So, ICA and Granger causality are not contradicting each other and finding causality btw sources would not have anything to do with source space or channel space data. In my point of view, using ICA and source signal for Granger causality is good because you do not have to worry about the volume conductance problem. However, one can apply Granger causality in the channel space as well since the dipole localization has its own limitations. One clue code be transforming the channel space data to current source density (CSD) format and then applying any causality/connectivity analysis you would like to study.</span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)"> </span><u></u><u></u></p><p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)">Best</span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)">Iman </span><u></u><u></u></p><p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)"> </span><u></u><u></u></p>
<p class="MsoNormal"><b><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)">-------------------------------------------------------------</span></b><u></u><u></u></p><p class="MsoNormal">
<b><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)">Iman M.Rezazadeh, Ph.D</span></b><u></u><u></u></p><p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)">Research Fellow</span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)">Semel Intitute, UCLA , Los Angeles</span><u></u><u></u></p><p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)">& Center for Mind and Brain, UC DAVIS, Davis</span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)"> </span><u></u><u></u></p><p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(31,73,125)"> </span><u></u><u></u></p>
<p class="MsoNormal"><b><span style="font-size:11pt;font-family:Calibri,sans-serif">From:</span></b><span style="font-size:11pt;font-family:Calibri,sans-serif"> <a href="mailto:eeglablist-bounces@sccn.ucsd.edu" target="_blank">eeglablist-bounces@sccn.ucsd.edu</a> [mailto:<a href="mailto:eeglablist-bounces@sccn.ucsd.edu" target="_blank">eeglablist-bounces@sccn.ucsd.edu</a>] <b>On Behalf Of </b>Makoto Miyakoshi<br>
<b>Sent:</b> Tuesday, February 18, 2014 3:54 PM<br><b>To:</b> <a href="mailto:mullen.tim@gmail.com" target="_blank">mullen.tim@gmail.com</a><br><b>Cc:</b> <a href="mailto:eeglablist@sccn.ucsd.edu" target="_blank">eeglablist@sccn.ucsd.edu</a><br>
<b>Subject:</b> Re: [Eeglablist] Two step source connectivity analysis (as implemented in SIFT)</span><u></u><u></u></p><div><div><p class="MsoNormal"> <u></u><u></u></p><div><p class="MsoNormal">Dear Tim,<u></u><u></u></p>
<div><p class="MsoNormal"> <u></u><u></u></p></div><div><p class="MsoNormal">Why don't you comment on the following question: If independent components are truly independent, how do causality analyses work?<u></u><u></u></p>
</div><div><p class="MsoNormal"> <u></u><u></u></p></div><div><p class="MsoNormal">Dear Joe,<u></u><u></u></p></div><div><p class="MsoNormal"> <u></u><u></u></p></div><div><p class="MsoNormal">Your inputs are too difficult for me to understand. In short, are you saying causality analysis works on independent components because they are not completely independent?<u></u><u></u></p>
</div><div><p class="MsoNormal"> <u></u><u></u></p></div><div><p class="MsoNormal">Makoto<u></u><u></u></p></div></div><div><p class="MsoNormal" style="margin-bottom:12pt"> <u></u><u></u></p><div><p class="MsoNormal">2014-02-18 15:46 GMT-08:00 Makoto Miyakoshi <<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>>:<u></u><u></u></p>
<blockquote style="border-style:none none none solid;border-left-color:windowtext;border-left-width:1pt;padding:0in 0in 0in 6pt;margin:5pt 0in 5pt 4.8pt">
<div><p class="MsoNormal">Dear Bethel,<u></u><u></u></p><div><div><p class="MsoNormal"> <u></u><u></u></p></div><div><p class="MsoNormal">> say A=sunrise and B=ice-cream-sale, then the ICA in EEGLAB should find that A is maximally temporaly independent from B.<u></u><u></u></p>
</div><div><p class="MsoNormal"> <u></u><u></u></p></div></div><div><p class="MsoNormal">ICA would find a correlation between sunrise and ice-cream-sale.<u></u><u></u></p></div><div><p class="MsoNormal"> <u></u><u></u></p>
</div><div><p class="MsoNormal">Makoto<u></u><u></u></p></div><div><p class="MsoNormal"> <u></u><u></u></p><div><p class="MsoNormal">2014-02-10 4:57 GMT-08:00 Bethel Osuagwu <<a href="mailto:b.osuagwu.1@research.gla.ac.uk" target="_blank">b.osuagwu.1@research.gla.ac.uk</a>>:<u></u><u></u></p>
<div><div><p class="MsoNormal"> <u></u><u></u></p><blockquote style="border-style:none none none solid;border-left-color:windowtext;border-left-width:1pt;padding:0in 0in 0in 6pt;margin:5pt 0in 5pt 4.8pt">
<p class="MsoNormal">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" target="_blank">eeglablist-bounces@sccn.ucsd.edu</a> [<a href="mailto:eeglablist-bounces@sccn.ucsd.edu" target="_blank">eeglablist-bounces@sccn.ucsd.edu</a>] On Behalf Of IMALI THANUJA HETTIARACHCHI [<a href="mailto:ith@deakin.edu.au" target="_blank">ith@deakin.edu.au</a>]<br>
Sent: 07 February 2014 01:27<br>To: <a href="mailto:mullen.tim@gmail.com" target="_blank">mullen.tim@gmail.com</a><br>Cc: <a href="mailto:eeglablist@sccn.ucsd.edu" target="_blank">eeglablist@sccn.ucsd.edu</a><br>Subject: [Eeglablist] Two step source connectivity analysis (as implemented in SIFT)<u></u><u></u></p>
<div><div><p class="MsoNormal"><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" target="_blank">+61430321972</a><u></u><u></u></p></div></div><p class="MsoNormal">Email: <a href="mailto:ith@deakin.edu.au" target="_blank">ith@deakin.edu.au</a><mailto:<a href="mailto:ith@deakin.edu.au" target="_blank">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><br>[<a>cid:image001.jpg@01CF23FF.F8259940</a>]<br>
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<div><p class="MsoNormal"><span style="color:rgb(136,136,136)">Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego</span><u></u><u></u></p>
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</div></div></blockquote></div><p class="MsoNormal"><br><br clear="all"><u></u><u></u></p><div><p class="MsoNormal"> <u></u><u></u></p></div><p class="MsoNormal">-- <u></u><u></u></p><div><p class="MsoNormal">Makoto Miyakoshi<br>
Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<u></u><u></u></p></div></div></div></div></div></div></blockquote></div><p class="MsoNormal"><br><br clear="all">
<u></u><u></u></p><div><p class="MsoNormal"><u></u> <u></u></p></div><p class="MsoNormal">-- <u></u><u></u></p><div><p class="MsoNormal">Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<u></u><u></u></p>
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-- <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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<br>-- <br></div></div>John Ochoa<br>
Docente de Bioingeniería<br>Universidad de Antioquia<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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