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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link=blue vlink=purple><div class=WordSection1><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>Hi, <o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>Sorry for using vague terms</span><span style='font-size:11.0pt;font-family:Wingdings;color:#1F497D'>J</span><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'> …Phase space and strange attractors are terms use in non-linear signal processing. Basically if you plot x(t) vs y(t+M) it is called phase space and the shape of that is called strange attractor. The extension or contraction of strange attractor could should some nature of event that is studied ( like how chaotic or non-linear or non-predictable is that event).<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>Suppose x(t)=Sin(2*pi*t/T) and y(t)=Cos(2*pi*t/T). x(t) and y(t) are perpendicular to each other, in other words they are independent/uncorrelated and their autocorrelation in ideal case is not a pure impulse function at zero – they are not noise- . Now, you can find a time lag T0 in which x(t) and Y(t+T0) are correlated – for simplicity if T0=T/4 then Cos will change to Sin.<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'> ~Iman<o:p></o:p></span></p><p class=MsoNormal><a name="_MailEndCompose"><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'><o:p> </o:p></span></a></p><p class=MsoNormal><b><span style='font-size:11.0pt;font-family:"Calibri","sans-serif"'>From:</span></b><span style='font-size:11.0pt;font-family:"Calibri","sans-serif"'> Makoto Miyakoshi [mailto:mmiyakoshi@ucsd.edu] <br><b>Sent:</b> Thursday, February 20, 2014 9:54 AM<br><b>To:</b> Iman M.Rezazadeh<br><b>Cc:</b> Andrei Medvedev; EEGLAB List<br><b>Subject:</b> Re: [Eeglablist] Two step source connectivity analysis (as implemented in SIFT)<o:p></o:p></span></p><p class=MsoNormal><o:p> </o:p></p><div><p class=MsoNormal>Dear Iman,<o:p></o:p></p><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>I did not understand your explanation. I don't know convenient terms to discuss it (I don't have an engineering background), so please use plain words.<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>My point is that neural signal has predictive patterns, which is what I meant auto-correlation. This means X(t) and X(t+1) are correlated, so are Y(t) and Y(t+1). If this holds, then it seems impossible to assume that although X'(t) and Y'(t) are independent X'(t) and Y'(t+1) are dependent. <o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>Dear Andrei,<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>I have a question about Nolte's claim. <o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>**********<o:p></o:p></p></div><div><div><p class=MsoNormal>The imaginary part of coherency is only sensitive to synchronizations of two processes which are time-lagged to each other. If volume conduction does not<o:p></o:p></p></div><div><p class=MsoNormal>cause a time-lag, the imaginary part of coherency is hence insensitive to artifactual ‘self-interaction’.<o:p></o:p></p></div></div><div><p class=MsoNormal>**********<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>I understand it. The assumption here is that source activity should be observed at different channels with the <i>same phase</i>. Am I correct? However, ERP researchers have observed inverted visual potentials in frontal channels, and also inverted N170 at vertex (Cz), and so on. Why these ERP components change phases depending on recording sites? I thought these are simple volume conduction.<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>'zero-delay' interaction is very interesting as you point. ICA is not good at capturing gamma (in my opinion) unless it is coupled with theta or other low-frequency activities. Our lab also reported broadband gamma (Onton and Makeig 2009) which is a different form of gamma from well-known gamma burst evoked by Kanitza illusions or moony faces.<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>It's a great opportunity for learning. Maybe my questions are naive and possibly based on wrong understanding. If I'm wrong I would appreciate if you tell me how I failed. Thank you very much.<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>Makoto<o:p></o:p></p></div></div><div><p class=MsoNormal style='margin-bottom:12.0pt'><o:p> </o:p></p><div><p class=MsoNormal>2014-02-19 14:34 GMT-08:00 Iman M.Rezazadeh <<a href="mailto:irezazadeh@ucdavis.edu" target="_blank">irezazadeh@ucdavis.edu</a>>:<o:p></o:p></p><blockquote style='border:none;border-left:solid #CCCCCC 1.0pt;padding:0in 0in 0in 6.0pt;margin-left:4.8pt;margin-right:0in'><div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>Thanks Andrei for elaborating this in more details. Also in my former post,</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>I forgot to mentioned the imaginary coherence method as suggested on Nolte et al. work and I agree with you on this as well. </span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>-Iman</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><a name="1444cb974198c7fd__MailEndCompose"><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'> </span></a><o:p></o:p></p><div><div style='border:none;border-top:solid #E1E1E1 1.0pt;padding:3.0pt 0in 0in 0in'><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><b><span style='font-size:11.0pt;font-family:"Calibri","sans-serif"'>From:</span></b><span style='font-size:11.0pt;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>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></span><o:p></o:p></p><div><div><p class=MsoNormal><br><b>Subject:</b> Re: [Eeglablist] Two step source connectivity analysis (as implemented in SIFT)<o:p></o:p></p></div></div></div></div><div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;margin-bottom:12.0pt'>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<o:p></o:p></p><pre>-- <o:p></o:p></pre><pre>Andrei Medvedev, PhD<o:p></o:p></pre><pre>Assistant Professor,<o:p></o:p></pre><pre>Center for Functional and Molecular Imaging<o:p></o:p></pre><pre>Georgetown University<o:p></o:p></pre><pre>4000 Reservoir Rd, NW<o:p></o:p></pre><pre>Washington DC, 20057<o:p></o:p></pre><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><br>On 2/19/2014 1:18 PM, Makoto Miyakoshi wrote: <o:p></o:p></p><blockquote style='margin-top:5.0pt;margin-bottom:5.0pt'><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>Dear Iman and all, <o:p></o:p></p><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>So are you saying independent sources can Granger cause each other?<o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>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.<o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>Note also that there is an issue of IC subspace within which ICs are always intra-dependent.<o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>Makoto <o:p></o:p></p></div></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;margin-bottom:12.0pt'> <o:p></o:p></p><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>2014-02-19 0:53 GMT-08:00 Iman M.Rezazadeh <<a href="mailto:irezazadeh@ucdavis.edu" target="_blank">irezazadeh@ucdavis.edu</a>>:<o:p></o:p></p><blockquote style='border:none;border-left:solid #CCCCCC 1.0pt;padding:0in 0in 0in 6.0pt;margin-left:4.8pt;margin-top:5.0pt;margin-right:0in;margin-bottom:5.0pt'><div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>I would like step in and add more comments which may be helpful (hopefully):</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'> </span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><a name="1444cb974198c7fd_144495a6d99af755__MailE"><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>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><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'> </span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>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><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'> </span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>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><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'> </span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>Best</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>Iman </span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'> </span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><b><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>-------------------------------------------------------------</span></b><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><b><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>Iman M.Rezazadeh, Ph.D</span></b><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>Research Fellow</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>Semel Intitute, UCLA , Los Angeles</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>& Center for Mind and Brain, UC DAVIS, Davis</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'> </span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'> </span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><b><span style='font-size:11.0pt;font-family:"Calibri","sans-serif"'>From:</span></b><span style='font-size:11.0pt;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><o:p></o:p></p><div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>Dear Tim,<o:p></o:p></p><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>Why don't you comment on the following question: If independent components are truly independent, how do causality analyses work?<o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>Dear Joe,<o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>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?<o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>Makoto<o:p></o:p></p></div></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;margin-bottom:12.0pt'> <o:p></o:p></p><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>2014-02-18 15:46 GMT-08:00 Makoto Miyakoshi <<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>>:<o:p></o:p></p><blockquote style='border:none;border-left:solid windowtext 1.0pt;padding:0in 0in 0in 6.0pt;margin-left:4.8pt;margin-top:5.0pt;margin-right:0in;margin-bottom:5.0pt;border-color:-moz-use-text-color -moz-use-text-color -moz-use-text-color rgb(204,204,204)'><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>Dear Bethel,<o:p></o:p></p><div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>> say A=sunrise and B=ice-cream-sale, then the ICA in EEGLAB should find that A is maximally temporaly independent from B.<o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p></div></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>ICA would find a correlation between sunrise and ice-cream-sale.<o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>Makoto<o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>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>>:<o:p></o:p></p><div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p><blockquote style='border:none;border-left:solid windowtext 1.0pt;padding:0in 0in 0in 6.0pt;margin-left:4.8pt;margin-top:5.0pt;margin-right:0in;margin-bottom:5.0pt;border-color:-moz-use-text-color -moz-use-text-color -moz-use-text-color rgb(204,204,204)'><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>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)<o:p></o:p></p><div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><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><o:p></o:p></p></div></div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>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 href="cid:image001.jpg@01CF23FF.F8259940">cid:image001.jpg@01CF23FF.F8259940</a>]<br><br><br><br><br><br><br>_______________________________________________<br>Eeglablist page: <a href="http://sccn.ucsd.edu/eeglab/eeglabmail.html" target="_blank">http://sccn.ucsd.edu/eeglab/eeglabmail.html</a><br>To unsubscribe, send an empty email to <a href="mailto:eeglablist-unsubscribe@sccn.ucsd.edu" target="_blank">eeglablist-unsubscribe@sccn.ucsd.edu</a><br>For digest mode, send an email with the subject "set digest mime" to <a href="mailto:eeglablist-request@sccn.ucsd.edu" target="_blank">eeglablist-request@sccn.ucsd.edu</a><o:p></o:p></p></blockquote></div></div></div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='color:#888888'><br><br clear=all></span><o:p></o:p></p><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p></div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='color:#888888'>-- </span><o:p></o:p></p><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='color:#888888'>Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego</span><o:p></o:p></p></div></div></div></blockquote></div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><br><br clear=all><o:p></o:p></p><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p></div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>-- <o:p></o:p></p><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<o:p></o:p></p></div></div></div></div></div></div></blockquote></div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><br><br clear=all><o:p></o:p></p><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p></div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>-- <o:p></o:p></p><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<o:p></o:p></p></div></div><pre> <o:p></o:p></pre><pre> <o:p></o:p></pre><pre>_______________________________________________<o:p></o:p></pre><pre>Eeglablist page: <a href="http://sccn.ucsd.edu/eeglab/eeglabmail.html" target="_blank">http://sccn.ucsd.edu/eeglab/eeglabmail.html</a><o:p></o:p></pre><pre>To unsubscribe, send an empty email to <a href="mailto:eeglablist-unsubscribe@sccn.ucsd.edu" target="_blank">eeglablist-unsubscribe@sccn.ucsd.edu</a><o:p></o:p></pre><pre>For digest mode, send an email with the subject "set digest mime" to <a href="mailto:eeglablist-request@sccn.ucsd.edu" target="_blank">eeglablist-request@sccn.ucsd.edu</a><o:p></o:p></pre></blockquote><p class=MsoNormal style='mso-margin-top-alt:auto;margin-bottom:12.0pt'><o:p> </o:p></p><pre> <o:p></o:p></pre></div></div></div></div><p class=MsoNormal><br>_______________________________________________<br>Eeglablist page: <a href="http://sccn.ucsd.edu/eeglab/eeglabmail.html" target="_blank">http://sccn.ucsd.edu/eeglab/eeglabmail.html</a><br>To unsubscribe, send an empty email to <a href="mailto:eeglablist-unsubscribe@sccn.ucsd.edu">eeglablist-unsubscribe@sccn.ucsd.edu</a><br>For digest mode, send an email with the subject "set digest mime" to <a href="mailto:eeglablist-request@sccn.ucsd.edu">eeglablist-request@sccn.ucsd.edu</a><o:p></o:p></p></blockquote></div><p class=MsoNormal><br><br clear=all><o:p></o:p></p><div><p class=MsoNormal><o:p> </o:p></p></div><p class=MsoNormal>-- <o:p></o:p></p><div><p class=MsoNormal>Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<o:p></o:p></p></div></div></div></body></html>