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<div style="direction: ltr;font-family: Tahoma;color: #000000;font-size: 10pt;">Dear Tianyang,
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<div>the spike timestamps in the fields ".timestamp" and ."time" should correspond to all trials that occur within the respective trial periods. In the spike-field coherence tutorial, if you compare the data structure for the entire recording (there called
"spike") with the trial-cut data structure (there called "spikeTrials"), you can see that there are less spikes in the latter. This is because those trials, which are not within the specified trial periods, are left out. </div>
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<div>The tutorial worked great for me. Unfortunately, it stops at the point where inferential stats have to be computed. <span style="font-size: 13.3333px;">I understand that I have to break up the relationship between spike timestamp per trial with lfp phase
per trial.</span><span style="font-size: 13.3333px;"> T</span><span style="font-size: 10pt;">his will require random resampling of the lfp-phases. </span><span style="font-size: 13.3333px;">Intuitively</span><span style="font-size: 10pt;">, I would shuffle
the trial vector for the lfp and then calculate the ppc for each random partition (using the same number of spikes). </span><span style="font-size: 10pt;">Let me add my own questions here. </span></div>
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<div>1) I<span style="font-size: 10pt;">t is unclear to me what happens when I have more than one spike per trial (e.g. 3 spikes in trial 1). Should I construct the resampling distribution with as many draws from each trial as there where in the original data
(e.g. 3 draws from trial 1 but at different time points)? </span></div>
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<div><span style="font-size: 10pt;">2) I wonder whether only the trial vector should be shuffled (preserving the time points per trial </span><span style="font-size: 10pt;">compared to the original effect but now from a different, random trial). In other words,
if I had in the original data 3 spikes which happened in the trials [1 2 3] and at the times [-.5 0 1], should the random partition look like this:</span></div>
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<div><span style="font-size: 10pt;">partition: trial = [3 1 2], time = [-.5 0 1]</span></div>
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<div><span style="font-size: 10pt;">or should the time points be taken completely at random? I hope, I was able to be clear enough with my explanation. </span></div>
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<div><span style="font-size: 10pt;">Best,</span></div>
<div><span style="font-size: 10pt;">Andreas</span></div>
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<div id="divRpF796279" style="direction: ltr;"><font face="Tahoma" size="2" color="#000000"><b>From:</b> fieldtrip-bounces@science.ru.nl [fieldtrip-bounces@science.ru.nl] on behalf of ÂíÌìÑô [tianyangma2013@gmail.com]<br>
<b>Sent:</b> Monday, August 28, 2017 5:05 AM<br>
<b>To:</b> fieldtrip@science.ru.nl<br>
<b>Subject:</b> Re: [FieldTrip] Using PPC method<br>
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<p class="MsoNormal"><span lang="EN-US">Dear Andreas,<span></span></span></p>
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<p class="MsoNormal"><span lang="EN-US">I am also a new learner using PPC method. In your reply to Julia, I don¡¯t quite understand why there are same number of values in timestamp and time, since timestamps is the spike times from the beginning of the recording
to the end and time is relative to trigger in each trial.<span></span></span></p>
<p class="MsoNormal"><span lang="EN-US"> </span></p>
<p class="MsoNormal"><span lang="EN-US">In my case, I have timestamp of spikes and LFP data 0.5 s before and after stimulus in each trial . Do you think it is possible I could use the code by somehow manipulating the format of input?
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<p class="MsoNormal"><span lang="EN-US">Thanks!<span></span></span></p>
<p class="MsoNormal"><span lang="EN-US"> </span></p>
<p class="MsoNormal"><span lang="EN-US">Best,<span></span></span></p>
<p class="MsoNormal"><span lang="EN-US">Tianyang<span></span></span></p>
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