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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 EEGLABers,<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>I have been thinking about this issue. Basically, we transfer the data from the sensor space to the source space to deal with the volume conductance problem and also dealing with less signal sources to create MVAR model. If we do MVAR model on electrode space then one might ask question about which electrode(s) to select and how to deal with VC problem? Also doing connectivity study on the sensor space might fraught with challenges in interpretation and high false positive rates. It has been shown that the combination of MVAR+BSS works compared to either BSS-alone or doing granger causality on the senor space.<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'>In addition , I have come up with an idea about the relation of Causality in the source space and sensor space:<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'>X(t)=M.S(t); where X(t) is the time series of Sensor space data for all electrodes and M is the Mixing matrix<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>S(t+1)=R S(t) +N(t) ; R is the Relationship Matrix btw Sources and N is the Noise matrix<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>M-1X(t+1)= R.M<sup>-1</sup>.X(t)+M-1N(t);<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>X(t+1)=M.R.M<sup>-1</sup>.X(t)+N(t);<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>X(t+1)=R’.X(t)+N(t);<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'>So, <o:p></o:p></span></p><p class=MsoListParagraph style='text-indent:-.25in;mso-list:l0 level1 lfo1'><![if !supportLists]><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'><span style='mso-list:Ignore'>1-<span style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span dir=LTR></span><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>the amount of the noise in the senor and source spaces does not change . <o:p></o:p></span></p><p class=MsoListParagraph style='text-indent:-.25in;mso-list:l0 level1 lfo1'><![if !supportLists]><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'><span style='mso-list:Ignore'>2-<span style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span dir=LTR></span><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>On the other hand there is a relationship btw Relation matrices in the sensor and source spaces which is M.R.M-1=R’. In general, R<> R’ but one can define M to have R=R’. <o:p></o:p></span></p><p class=MsoListParagraph style='text-indent:-.25in;mso-list:l0 level1 lfo1'><![if !supportLists]><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'><span style='mso-list:Ignore'>3-<span style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span dir=LTR></span><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>The point is in the source space you can great R with a few number of dipoles however in the electrode space the relationship btw the electrodes is not clear, but I it might be the case that the electrodes that have high mixing values are more correlated to each other ( it needs some simulation and test). <o:p></o:p></span></p><p class=MsoListParagraph style='text-indent:-.25in;mso-list:l0 level1 lfo1'><![if !supportLists]><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'><span style='mso-list:Ignore'>4-<span style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span dir=LTR></span><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>I think calculation coherence in the sensor space makes more sense but if we want to do causality analysis and MVAR modeling source space is more applicable. So, the ‘elimination’ of phase relationship in the sensor space may cause alternation in coherence but it does not do anything with the causal relationship btw sources and in given condition electrodes.<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'>These are my thought and would be happy to hear more comments !<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>Best,<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><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'> <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><b><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>From:</span></b><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'> eeglablist-bounces@sccn.ucsd.edu [mailto:eeglablist-bounces@sccn.ucsd.edu] <b>On Behalf Of </b>Adrian Willoughby<br><b>Sent:</b> Monday, July 15, 2013 10:04 PM<br><b>To:</b> mmiyakoshi@ucsd.edu<br><b>Cc:</b> eeglablist@sccn.ucsd.edu<br><b>Subject:</b> Re: [Eeglablist] sift and phase<o:p></o:p></span></p><p class=MsoNormal><o:p> </o:p></p><div><p class=MsoNormal>Hi all,<o:p></o:p></p><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>I've been thinking about this recently too and came across this - which may be a reasonable place to start the discussion.<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal><a href="http://www.appliedneuroscience.com/COHERENCE-DevNeuropsy-WEB.pdf">http://www.appliedneuroscience.com/COHERENCE-DevNeuropsy-WEB.pdf</a><o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>I'd also be interested in hearing other's opinions.<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>Adrian<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>On Mon, Jul 15, 2013 at 7:32 PM, Makoto Miyakoshi <<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>> wrote:<o:p></o:p></p><div><p class=MsoNormal>Dear Robert,<o:p></o:p></p><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>I've seen the same claim in this list, so you may want to find it in the archive. I did not find any convincing argument in their claim. If you know other papers I'd be interested in knowing.<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><div><div><p class=MsoNormal style='margin-bottom:12.0pt'><o:p> </o:p></p><div><p class=MsoNormal>2013/7/15 robert coben <<a href="mailto:drcoben@gmail.com" target="_blank">drcoben@gmail.com</a>><o:p></o:p></p><p class=MsoNormal>We have recently started using sift for multivariate and directional coherence analyses with very interesting findings, at least initially. My question is this. There are some critics of ICA that suggest it alters phase relationships in the EEG rendering any coherence or network analyses invalid. I would love to hear the opinion of those in this group about this notion.<br><br>Thanks,<br><br>Rob<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></div><p class=MsoNormal><br><br clear=all><o:p></o:p></p><div><p class=MsoNormal><o:p> </o:p></p></div></div></div><p class=MsoNormal><span class=hoenzb><span style='color:#888888'>-- <o:p></o:p></span></span></p><div><p class=MsoNormal><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><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></div><p class=MsoNormal><o:p> </o:p></p></div></div></body></html>