<html xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="urn:schemas-microsoft-com:office:word" xmlns:m="http://schemas.microsoft.com/office/2004/12/omml" xmlns="http://www.w3.org/TR/REC-html40"><head><meta http-equiv=Content-Type content="text/html; charset=us-ascii"><meta name=Generator content="Microsoft Word 15 (filtered medium)"><!--[if !mso]><style>v\:* {behavior:url(#default#VML);}
o\:* {behavior:url(#default#VML);}
w\:* {behavior:url(#default#VML);}
.shape {behavior:url(#default#VML);}
</style><![endif]--><style><!--
/* Font Definitions */
@font-face
{font-family:"Cambria Math";
panose-1:2 4 5 3 5 4 6 3 2 4;}
@font-face
{font-family:Calibri;
panose-1:2 15 5 2 2 2 4 3 2 4;}
@font-face
{font-family:Tahoma;
panose-1:2 11 6 4 3 5 4 4 2 4;}
/* Style Definitions */
p.MsoNormal, li.MsoNormal, div.MsoNormal
{margin:0in;
margin-bottom:.0001pt;
font-size:12.0pt;
font-family:"Times New Roman","serif";}
a:link, span.MsoHyperlink
{mso-style-priority:99;
color:blue;
text-decoration:underline;}
a:visited, span.MsoHyperlinkFollowed
{mso-style-priority:99;
color:purple;
text-decoration:underline;}
span.EmailStyle17
{mso-style-type:personal-reply;
font-family:"Calibri","sans-serif";
color:#1F497D;}
.MsoChpDefault
{mso-style-type:export-only;
font-family:"Calibri","sans-serif";}
@page WordSection1
{size:8.5in 11.0in;
margin:1.0in 1.0in 1.0in 1.0in;}
div.WordSection1
{page:WordSection1;}
--></style><!--[if gte mso 9]><xml>
<o:shapedefaults v:ext="edit" spidmax="1026" />
</xml><![endif]--><!--[if gte mso 9]><xml>
<o:shapelayout v:ext="edit">
<o:idmap v:ext="edit" data="1" />
</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'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>Think about this : <o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>X(t=t1)=[x1(t1) x2(t1)]; <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'>Y(t=t1)=[y1(t1) y2(t1)];<o:p></o:p></span></a></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>These two vectors at t=t1 can be uncorrelated/independent/….<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'>Now, X(t=t2) =[ x1(t2) x2(t2)] can be correlated/dependent/… to X(t=t1) or Y(t=t2). A simple experiment could be to sketch the phase/state space of variable X, Y and also joint phase space ( Y(t+m) vs. X(t) ). my though is if X and Y are not noise/random – they are not if you are considering two “neural” sources - then the joint strange attractor should be limited to a region in the phase space and on can find an m which leads to more condense strange attractor.<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'>P.S.: I do not get your point when you say “ </span>given that X(t) has auto-correlation?”<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><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: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> Wednesday, February 19, 2014 12:27 PM<br><b>To:</b> Iman M.Rezazadeh<br><b>Cc:</b> Aleksandra Vuckovic; 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>Is it possible that X(t) and Y(t) are independent but X(t) and Y(t+1) are dependent, given that X(t) has auto-correlation?<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 11:44 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'>Hi Makoto and all, </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'>Actually I am thinking about ICA and GC without considering their applications in ERP/EEG. What I said is the IC sources are “independent’ at time point t but ICA does not guarantee to remove any dependency between event X and Y at different time points like X(t) and (Y+1) </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="1444bb36640563fb__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"'> Aleksandra Vuckovic [mailto:<a href="mailto:Aleksandra.Vuckovic@glasgow.ac.uk" target="_blank">Aleksandra.Vuckovic@glasgow.ac.uk</a>] <br><b>Sent:</b> Wednesday, February 19, 2014 11:31 AM<br><b>To:</b> <a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>; Iman M.Rezazadeh<br><b>Cc:</b> EEGLAB List<br><b>Subject:</b> RE: [Eeglablist] Two step source connectivity analysis (as implemented in SIFT)</span><o:p></o:p></p></div></div><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'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>Hi</span><o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>we've looked at Granger causality of ICAs during motor imagery task, yes you can definitively see that some sources 'speak' to each other at certain points of time, related to the event while some other seem to be there always (in mu rhythm) independent on the event.</span><o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>Regards,</span><o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>Alex</span><o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'> </span><o:p></o:p></p></div><div><div class=MsoNormal align=center style='text-align:center'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'><hr size=3 width="100%" align=center></span></div><p class=MsoNormal style='mso-margin-top-alt:auto;margin-bottom:12.0pt'><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"'> <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 Makoto Miyakoshi [<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>]<br><b>Sent:</b> 19 February 2014 18:18<br><b>To:</b> Iman M.Rezazadeh<br><b>Cc:</b> EEGLAB List<br><b>Subject:</b> Re: [Eeglablist] Two step source connectivity analysis (as implemented in SIFT)</span><o:p></o:p></p></div><div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>Dear Iman and all, </span><o:p></o:p></p><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'> </span><o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>So are you saying independent sources can Granger cause each other?</span><o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'> </span><o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>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.</span><o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'> </span><o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>Note also that there is an issue of IC subspace within which ICs are always intra-dependent.</span><o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'> </span><o:p></o:p></p></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>Makoto </span><o:p></o:p></p></div></div><div><p class=MsoNormal style='mso-margin-top-alt:auto;margin-bottom:12.0pt'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'> </span><o:p></o:p></p><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>2014-02-19 0:53 GMT-08:00 Iman M.Rezazadeh <<a href="mailto:irezazadeh@ucdavis.edu" target="_blank">irezazadeh@ucdavis.edu</a>>:</span><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'> <o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><a name="1444bb36640563fb_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'> <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'> <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'> <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'> <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'> <o:p></o:p></p><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;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 #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><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 #CCCCCC 1.0pt;padding:0in 0in 0in 6.0pt;margin-left:4.8pt;margin-top:5.0pt;margin-right:0in;margin-bottom:5.0pt'><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'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'><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'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'> </span><o:p></o:p></p></div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>-- </span><o:p></o:p></p><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>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></div></div></div></div></div></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>