<div dir="ltr"><div class="gmail_extra"><div class="gmail_quote"><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">
Dear Hwee, <br></blockquote><div> In your case, you can calculate the phase consistency across trials at each frequency, thus , you don't temporal domain information within each trial. <br></div><div> I recommend you to use imagery part of coherence since it is insensitive to the volume conduction . In FT . you can specify <span style> ft_connectivityanalysis with cfg.output = 'imag'. After this , you get chan*Chan connectivity matrix . Regarding to the statistic, you can do permutation test by randomize the time sequence within each trial 1000 times then calculate randomized chan*chan connectivity distribution. In the end ,you can use cluster-statistic framework to find the </span>synchronized channel clusters. I don't know how to incorporate within FT. </div>
<div> </div><div> Hope it helps,</div><div> Haiteng</div>
<div> </div><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">
Message: 7<br>
Date: Tue, 8 Apr 2014 15:56:09 +0200<br>
From: Hwee Ling Lee <<a href="mailto:hweeling.lee@gmail.com" target="_blank">hweeling.lee@gmail.com</a>><br>
To: FieldTrip discussion list <<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a>><br>
Subject: [FieldTrip] Investigating phase synchronisation in resting<br>
state eeg data<br>
Message-ID:<br>
<CABG7z0Tx7KHHUbdjbFKX3xeBTNRR1=_<a href="mailto:Z-zVfb2S-Zovo8_k9pw@mail.gmail.com" target="_blank">Z-zVfb2S-Zovo8_k9pw@mail.gmail.com</a>><br>
Content-Type: text/plain; charset="iso-8859-1"<br>
<br>
Dear all,<br>
<br>
I'm investigating phase synchronisation on resting state eeg data. However,<br>
I'm not sure if what I'm doing is appropriate. It will be great if someone<br>
could help me on this.<br>
<br>
So, I've segmented my resting state data into smaller chunks of 10240<br>
timepoints, which is approximately 2s per trial for my dataset.<br>
<br>
Since I'm mainly interested to know the phase changes at each frequency<br>
band, I thought the best way is to perform a mtmfft, using a hanning taper,<br>
with 'fourier' as output. I then performed a connectivity analysis on all<br>
the channels paired with all channels. Is this appropriate at all? Can one<br>
look at phase changes at each frequency band without taking account into<br>
time? Or should one look at phase changes at each frequency band along the<br>
temporal domain (time, in this case, it would be 2 s per trial)?<br></blockquote><div> </div><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">
<br>
Also, I noticed that there's the possibility to calculate phase locking<br>
value (PLV) and weighted paired wise consistency (WPPC). What are the<br>
differences between these two methods? As I'm interested to know which pair<br>
of channels are phase synchronised, how should I perform the statistical<br>
calculation? Also, how do I represent the data in this case, given that my<br>
reference channel is all against all channels?<br>
<br>
Thanks.<br>
<br>
Best regards,<br>
Hweeling<br>
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</blockquote></div><br><br clear="all"><div><br></div>-- <br><div><div><font face="arial, helvetica, sans-serif">Haiteng Jiang</font></div><div><font face="arial, helvetica, sans-serif">PhD candidate</font></div><div><font face="arial, helvetica, sans-serif">Neuronal Oscillations Group</font></div>
<div><font face="arial, helvetica, sans-serif">Donders Institute for Brain, Cognition and Behaviour</font></div><div><font face="arial, helvetica, sans-serif">Centre for Cognitive Neuroimaging</font></div><div><font face="arial, helvetica, sans-serif">Radboud University Nijmegen</font></div>
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