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<div dir="ltr">Dear FT Experts<div><br></div><div>Thank you for your inputs. I should add here that I have continuous EEG data (so effectively single trial). The way I am computing PLV is by dividing my data into windows of 2 seconds each and computing PLV for each window and then I finally average the PLV values of all the windows.</div><div><br></div><div>So when we talk of shuffling trials, in my case should i consider each of 2 second window as a trial ? Sorry if this sounds stupid as I am new to this kind of analysis.<br><br><div><hr id="stopSpelling">From: smoratti@psi.ucm.es<br>Date: Sat, 18 May 2013 20:11:11 +0200<br>To: fieldtrip@science.ru.nl<br>Subject: Re: [FieldTrip] computing threshold for PLV<br><br>Dear Sheraz,<div><br></div><div>You might consult the following article: Lachaux, J. P. et al. 1999. Measuring Phase Synchrony in Brain Signal. Human Brain Mapping 8: 194-208. In principle you have to create surrogate data that represent the null hypothesis that there is no phase locking between your electrodes, sources, etc. Then you create hundreds or thousands of surrogate data sets and calculate each time you PLV. That way you create a distribution of your PLV values under the Null hypothesis. Then you check in what percentile your observed PLV lies (e. g. if its beyond the 95% percentile you would reject the Null hypothesis).</div><div><br></div><div>Hope that helps,</div><div><br></div><div>Stephan</div><div><br></div><div><br></div><div><br><div>
<span class="ecxApple-style-span" style="border-collapse:separate;color:rgb(0, 0, 0);font-family:Helvetica;font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;orphans:2;text-align:-webkit-auto;text-indent:0px;text-transform:none;white-space:normal;widows:2;word-spacing:0px;font-size:medium;"><div><div>________________________________________________________</div><div>Stephan Moratti, PhD<br><br></div><div>see also: <a href="http://web.me.com/smoratti/" target="_blank">http://web.me.com/smoratti/</a><br><br></div><div>Universidad Complutense de Madrid</div><div>Facultad de Psicología</div><div>Departamento de Psicología Básica I</div><div>Campus de Somosaguas</div><div>28223 Pozuelo de Alarcón (Madrid)</div><div>Spain</div><div><br></div><div>and</div><div><br></div><div>Center for Biomedical Technology</div><div>Laboratory for Cognitive and Computational Neuroscience</div><div>Parque Científico y Tecnológico de la Universidad Politecnica de Madrid<br>Campus Montegancedo</div><div>28223 Pozuelo de Alarcón (Madrid)</div><div>Spain<br><br><br>email: <a href="mailto:smoratti@psi.ucm.es">smoratti@psi.ucm.es</a><br>Tel.: +34 679219982</div></div></span>
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<br><div><div>El 18/05/2013, a las 18:21, Subramaniam Iyer escribió:</div><br class="ecxApple-interchange-newline"><blockquote><span class="ecxApple-style-span" style="border-collapse:separate;font-family:Helvetica;font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;orphans:2;text-align:-webkit-auto;text-indent:0px;text-transform:none;white-space:normal;widows:2;word-spacing:0px;font-size:medium;"><div class="ecxhmmessage" style="font-size:12pt;font-family:Calibri;"><div dir="ltr">Dear FT Experts,<br><br>I have calculated PLV for a set of EEG data from 5 different patients. Now I want to convert the PLV matrix of each patient into a binary matrix. For this I need a threshold PLV value below which I can assume the phase locking is zero.<br>My question is, how do I compute this threshold. I know hard thresholding is one option ( for ex setting 0.1 or 0.2 as threshold), but I guess it is not a very good option.<br><br>Can somebody suggest a better and robust (statistical) way of determining the threshold ? </div>_______________________________________________<br>fieldtrip mailing list<br><a href="mailto:fieldtrip@donders.ru.nl">fieldtrip@donders.ru.nl</a><br><a href="http://mailman.science.ru.nl/mailman/listinfo/fieldtrip" target="_blank">http://mailman.science.ru.nl/mailman/listinfo/fieldtrip</a></div></span></blockquote></div><br></div><br>_______________________________________________
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