<div dir="ltr">Dear Mahyar,<div><br></div><div>Yes, SIFT has decent statistics, but I often experienced memory flow so be careful. I don't know if it has a dedicated manual for statistics.</div><div><br></div><div>If you open EEG.CAT.conn.... you can find SIFT results stored with which you can run your own statistics. I always do that.</div>
<div><br></div><div>Makoto</div></div><div class="gmail_extra"><br><br><div class="gmail_quote">On Thu, Jul 10, 2014 at 3:33 AM, <span dir="ltr"><<a href="mailto:mahyar_hamedi@yahoo.com" target="_blank">mahyar_hamedi@yahoo.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div dir="ltr">
<div dir="ltr" style="font-family:'Calibri','Arial','Segoe UI','Meiryo','Microsoft YaHei UI','Microsoft JhengHei UI','Malgun Gothic';font-size:12pt"><div>Dear List,</div>
<div><br></div><div>I couldn’t find any clear manual and notes on surrogate statistics implemented in SIFT. I am trying to find the estimated connectivities that are statistically significant (at P<0.01) after contrast with surrogate distribution. Is it possible to do that in SIFT? I’d appreciate your help in this regard. </div>
<div><br></div><div>Regards, <font face="Times New Roman">
</font> </div><div><br></div></div>
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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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