<div dir="ltr">I can point you to the SIFT manual written by Tim.<div><br></div><div>Without checking the paper you sent me, as long as the tool is advertised as multivariate, it should address the spurious connectivity problem. My point was that if you repeat GC without addressing this issue you will get wrong results.<div>
<br></div><div>Makoto</div></div></div><div class="gmail_extra"><br><br><div class="gmail_quote">On Mon, Jul 28, 2014 at 5:00 PM, Iman M.Rezazadeh <span dir="ltr"><<a href="mailto:irezazadeh@ucdavis.edu" target="_blank">irezazadeh@ucdavis.edu</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div lang="EN-US" link="blue" vlink="purple"><div><p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1f497d">I think it depends on how we calculate GC and also eliminate the volume conductance problem. Have you looked at the following paper? Also, it would be appreciated if you please cite some papers for your logic/statement</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"> <u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:13.5pt">The MVGC multivariate Granger causality toolbox: A new approach to Granger-causal inference </span><span style="font-size:10.5pt">Lionel Barnett</span><span style="font-size:7.0pt;font-family:"ADCEI A+ MTSY","sans-serif"">∗</span><span style="font-size:10.5pt">, Anil K. Seth</span><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1f497d"><u></u><u></u></span></p>
<p class="MsoNormal"><a name="1477f6b50063a6da__MailEndCompose"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1f497d"><u></u> <u></u></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:<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>] <br>
<b>Sent:</b> Monday, July 28, 2014 4:43 PM<br><b>To:</b> Iman M.Rezazadeh<br><b>Cc:</b> Salim Al-wasity; EEGLAB List</span></p><div><div class="h5"><br><b>Subject:</b> Re: [Eeglablist] Question about the use of ICA and GC<u></u><u></u></div>
</div><p></p><div><div class="h5"><p class="MsoNormal"><u></u> <u></u></p><div><p class="MsoNormal">Dear Iman,<u></u><u></u></p><div><p class="MsoNormal"><u></u> <u></u></p></div><div><p class="MsoNormal">Because it can detect spurious connections that should be suppressed.<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><p class="MsoNormal" style="margin-bottom:12.0pt"><u></u> <u></u></p><div><p class="MsoNormal">On Mon, Jul 28, 2014 at 3:49 PM, Iman M.Rezazadeh <<a href="mailto:irezazadeh@ucdavis.edu" target="_blank">irezazadeh@ucdavis.edu</a>> wrote:<u></u><u></u></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"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1f497d">Hi Makoto, </span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1f497d">Could you please let me know how do you get to this point “</span><span style="color:black">By the way if you have more than 3 ICs for GC don't use GC but use RPDC or normalized dDTF” ?</span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1f497d">Thanks ! </span><u></u><u></u></p><p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1f497d">Iman</span><u></u><u></u></p>
<p class="MsoNormal"><a name="1477f6b50063a6da_1477f2a4bf1869f3__MailEndCompose"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1f497d"> </span></a><u></u><u></u></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""> <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> Monday, July 28, 2014 3:30 PM<br><b>To:</b> Salim Al-wasity<br><b>Cc:</b> <a href="mailto:eeglablist@sccn.ucsd.edu" target="_blank">eeglablist@sccn.ucsd.edu</a><br><b>Subject:</b> Re: [Eeglablist] Question about the use of ICA and GC</span><u></u><u></u></p>
<div><div><p class="MsoNormal"> <u></u><u></u></p><div><p class="MsoNormal">Dear Salim,<u></u><u></u></p><div><p class="MsoNormal"> <u></u><u></u></p></div><div><div><p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black">> 1- Are the predicted S1(60) and S2(60) independent as the actual ones which obtained using ICA.</span><u></u><u></u></p>
</div><div><p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black"> </span><u></u><u></u></p></div><div><p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black">If the S1(60) and S2(60) are the predictions made by i.e. AR model from S1(1-59) and S2(1-59) respectively, then they are most likely independent.</span><u></u><u></u></p>
</div><div><p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black"> </span><u></u><u></u></p></div><div><p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black">> 2- The past values that it used to predict S1(60) and S2(60) are independent. Does this independence affect the predict variables?</span><u></u><u></u></p>
</div></div><div><p class="MsoNormal"> <u></u><u></u></p></div><div><p class="MsoNormal">Yes, why not?<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">On Sun, Jul 27, 2014 at 8:22 AM, Salim Al-wasity <<a href="mailto:salim_alwasity@yahoo.com" target="_blank">salim_alwasity@yahoo.com</a>> wrote:<u></u><u></u></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><div><p class="MsoNormal" style="background:white">
<span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black">Dear Mr. Miyakoshi</span><u></u><u></u></p></div><div><p class="MsoNormal" style="background:white"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black">Thanks for your reply. Let assumes that an ICA is applied to an observe matrix X (of 2 channels and 100 samples), therefore an S decomposed independent source signal is obtain.</span><u></u><u></u></p>
</div><div><p class="MsoNormal" style="background:white"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black">After that GC is used to find the connectivity between S1 and S2 (channel 1 and channel 2 of S), for instance to predict S1(60) and S2(60) based on the past values of S1 and S2 (for a predefined order), then:</span><u></u><u></u></p>
</div><div><p class="MsoNormal" style="background:white"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black">1- Are the predicted S1(60) and S2(60) independent as the actual ones which obtained using ICA.</span><u></u><u></u></p>
</div><div><p class="MsoNormal" style="background:white"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black">2- The past values that it used to predict S1(60) and S2(60) are independent. Does this independence affect the predict variables?</span><u></u><u></u></p>
</div><div><p class="MsoNormal" style="background:white"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black"> </span><u></u><u></u></p></div><div><p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black">Sincerely</span><u></u><u></u></p>
</div><div><p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black">Salim</span><u></u><u></u></p></div><div><div><div><p class="MsoNormal" style="margin-bottom:12.0pt;background:white">
<span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black"> </span><u></u><u></u></p></div><div><div><div><div><p class="MsoNormal" style="background:white"><span style="font-family:"Arial","sans-serif";color:black">On Friday, 25 July 2014, 20:41, Makoto Miyakoshi <<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>> wrote:</span><u></u><u></u></p>
</div><p class="MsoNormal" style="margin-bottom:12.0pt;background:white"><span style="color:black"> </span><u></u><u></u></p><div><div><div><div><p class="MsoNormal" style="background:white"><span style="color:black">Dear Salim,</span><u></u><u></u></p>
<div><p class="MsoNormal" style="background:white"><span style="color:black"> </span><u></u><u></u></p></div><div><p class="MsoNormal" style="background:white"><span style="color:black">ICA assures <i>instantaneous</i> independence, while GC calculates <i>temporal</i> causality. This means that ICA does not know what happens in the next moment, but GC does.</span><u></u><u></u></p>
</div><div><p class="MsoNormal" style="background:white"><span style="color:black"> </span><u></u><u></u></p></div><div><p class="MsoNormal" style="background:white"><span style="color:black">By the way if you have more than 3 ICs for GC don't use GC but use RPDC or normalized dDTF.</span><u></u><u></u></p>
</div><div><p class="MsoNormal" style="background:white"><span style="color:black"> </span><u></u><u></u></p></div><div><p class="MsoNormal" style="background:white"><span style="color:black">Makoto</span><u></u><u></u></p>
</div></div><div><p class="MsoNormal" style="margin-bottom:12.0pt;background:white"><span style="color:black"> </span><u></u><u></u></p><div><p class="MsoNormal" style="background:white"><span style="color:black">On Fri, Jul 25, 2014 at 4:41 AM, Salim Al-wasity <<a href="mailto:salim_alwasity@yahoo.com" target="_blank">salim_alwasity@yahoo.com</a>> wrote:</span><u></u><u></u></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><div><div><p class="MsoNormal" style="background:white">
<span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black">Dears</span><u></u><u></u></p></div><div><p class="MsoNormal" style="background:white"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black">Have a nice day. Kindly I have a question regarding ICA:<br>
I have EEG data of (44 channels X 294000 samples)<br><br>1- I applied the ICA to separate the noise and find the ICs which are belong to brain activities.<br>2- I used the Granger Causality (GC) in SIFT to find the connectivity between these ICs and discover which component influence which. However I am not sure about the results that I have got.</span><u></u><u></u></p>
</div><div><p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black"><br>My question is that If I used ICA to decompose the EEG signal into their sources (hint: the decomposed ICs is much less than the actual brain sources), theoretically these ICs would be independent, and the use of GC would be useless since the latter algorithm search for the dependence across ICs?<br>
Or the ICA will minimize the mutual information, and the separated components will not be ~100% independent, therefore each component has more that one source, and GC can find some influence across these components for the remaining not separated sources. </span><u></u><u></u></p>
</div><div><p class="MsoNormal" style="background:white"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black"> </span><u></u><u></u></p></div><div><p class="MsoNormal" style="background:white">
<span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black">Your cooperation is highly appreciated</span><u></u><u></u></p></div><div><p class="MsoNormal" style="background:white"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black"> </span><u></u><u></u></p>
</div><div><p class="MsoNormal" style="background:white"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black">Yours</span><u></u><u></u></p></div><div><p class="MsoNormal" style="background:white">
<span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black">Salim Al-Wasity</span><u></u><u></u></p></div><div><p class="MsoNormal" style="background:white"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black">PhD student</span><u></u><u></u></p>
</div><div><p class="MsoNormal" style="background:white"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black">Rehabilitation Centre</span><u></u><u></u></p></div><div><p class="MsoNormal" style="background:white">
<span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black">Biomedical Engineering Department-School of Engineering</span><u></u><u></u></p></div><div><p class="MsoNormal" style="background:white">
<span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black">University of Glasgow</span><u></u><u></u></p></div><div><p class="MsoNormal" style="background:white"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black">Glasgow-United Kingdom</span><u></u><u></u></p>
</div><div><p class="MsoNormal" style="background:white"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:black"><a href="tel:%2B44%20742%20371%204444" target="_blank">+44 742 371 4444</a></span><u></u><u></u></p>
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</blockquote></div><p class="MsoNormal" style="background:white"><span style="color:black"><br><br clear="all"></span><u></u><u></u></p><div><p class="MsoNormal" style="background:white"><span style="color:black"> </span><u></u><u></u></p>
</div><p class="MsoNormal" style="background:white"><span style="color:black">-- </span><u></u><u></u></p><div><p class="MsoNormal" style="background:white"><span style="color:black">Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>
Institute for Neural Computation, University of California San Diego</span><u></u><u></u></p></div></div></div></div><p class="MsoNormal" style="margin-bottom:12.0pt;background:white"><span style="color:black"> </span><u></u><u></u></p>
</div></div></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></div></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></div></div></div></div></div></div></div></div></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>
</div>