<html><head><meta http-equiv="Content-Type" content="text/html charset=windows-1252"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;"><div>HI,</div><div>you could check out this approach: <a href="http://www.fieldtriptoolbox.org/tutorial/networkanalysis">http://www.fieldtriptoolbox.org/tutorial/networkanalysis</a>. You can compute your desired connectivity metric during the call to ft_connectivity analysis. Many of the issues stressed in the paper still apply though, independent of the metric of your choice. You could also segment your data on the basis of some event you consider relevant, e.g. particular phases of an ongoing signal. It is your model that you believe represents a good “guess” of the cerebral connectivity pattern not so much the metric. </div><div><br></div><div>good luck</div><div><br></div><div>tzvetan</div><div><br></div><div><br></div><div><br class="Apple-interchange-newline"><blockquote type="cite"><div dir="ltr">Dear Tzvetan,<br><br>thanks for your reply. However the reference you kindly provided doesn't address the estimation of connectivity in the case of continuous data (all mentioned measures seem to require being fed with multiple trials data). Beside, it is a good tutorial but not really exhaustive (for instance not a word on WPLI).<br><div><br></div><div class="gmail_extra"><br clear="all"><div><div data-smartmail="gmail_signature"><div dir="ltr"><b style="color:rgb(153,153,153)">Jonas </b></div></div></div>
<br><div class="gmail_quote">2016-07-22 12:07 GMT+02:00 Tzvetan Popov <span dir="ltr"><<a href="mailto:tzvetan.popov@uni-konstanz.de" target="_blank">tzvetan.popov@uni-konstanz.de</a>></span>:<br><blockquote class="gmail_quote" style="margin: 0px 0px 0px 0.8ex; border-left-width: 1px; border-left-color: rgb(204, 204, 204); border-left-style: solid; padding-left: 1ex; position: static; z-index: auto;"><div style="word-wrap:break-word"><div>Dear Jonas,</div><div><br></div><div>this paper is quite a good start: <a href="http://journal.frontiersin.org/article/10.3389/fnsys.2015.00175/full" target="_blank">http://journal.frontiersin.org/article/10.3389/fnsys.2015.00175/full</a></div><div>Very accessible read also providing some playground data to begin with. </div><div>Good luck,</div><div>tzvetan</div><div> </div><div><br></div><div><br><br><blockquote type="cite"><div><div dir="ltr">Dear Fieldtrippers,<div><br></div><div>In terms of connectivity measure for EEG signals, WPLI appears to have a good reputation among the neurophysio community. However it seems to require multiple trials for its estimation (e.g., <a href="https://mailman.science.ru.nl/pipermail/fieldtrip/2015-August/009575.html" target="_blank">https://mailman.science.ru.nl/pipermail/fieldtrip/2015-August/009575.html</a>). Could it be applied on continuous data (e.g., >1 minute resting state segments) and, if not, what could be an appropriate connectivity measure in this context?</div><div><br></div><div>best, </div><div><br></div><div><br clear="all"><div><div data-smartmail="gmail_signature"><div dir="ltr"><b style="color:rgb(153,153,153)">Jonas </b></div></div></div>
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