Dear eeglablist,<div><br></div><div>I'm just computing EEG data using EEGLAB. However, I met some problems about pre-computing ERSPs/ITC (channel measures) and hope someone could help me. </div><div><br></div><div>In my experiment, I had 43 subjects, 500Hz sampling rate. It was a 2 by 2 repeated measures design, and each condition had about 30 trials and the same 43 channels for each subject after rejecting. I pre-processed the raw data in Brain Vision Analyzer until I extracted epochs for each condition which was -200 to 800ms. I exported the epoched data to EEGLAB, re-saved it as .set and .fdt files for each condition each subject, and created a STUDY using those data. Then I just used the GUI and tried to pre-compute the data. </div>
<div><br></div><div>For ERP part, it worked well and got the same result with Analyzer. But for ERSPs/ITS part, it didn't. Actually, I do not quite understand the parameters of ERSPs/ITC processing, so I just used the default. However, I found that it only computed 23.4Hz to 250Hz's data, which do not include my interest Frequency(3 to 30Hz). I tried to add options in GUI like " 'cycles',[3 0.5], 'nfreqs', 100, 'freqs',[3 30] ",but it turned out as a wrong syntax.</div>
<div><br></div><div>When I used the default parameter, it showed in the MATLAB command window like this below. I was just wondering what the red sentence means ( <font color="#ff0000">1) to 4) </font>) and hope that someone could tell me what should I do if I just want it to compute 3Hz to 30Hz frequency.</div>
<div><br></div><blockquote style="margin:0 0 0 40px;border:none;padding:0px"><blockquote style="margin:0 0 0 40px;border:none;padding:0px"><div><div>Computing Event-Related Spectral Perturbation (ERSP) and</div></div></blockquote>
<blockquote style="margin:0 0 0 40px;border:none;padding:0px"><div><div> Inter-Trial Phase Coherence (ITC) images based on 31 trials</div></div></blockquote><blockquote style="margin:0 0 0 40px;border:none;padding:0px">
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<div> of 500 frames sampled at 500 Hz.</div></div></blockquote><blockquote style="margin:0 0 0 40px;border:none;padding:0px"><div><div>Each trial contains samples from -200 ms before to</div></div></blockquote><blockquote style="margin:0 0 0 40px;border:none;padding:0px">
<div><div> 798 ms after the timelocking event.</div></div></blockquote><blockquote style="margin:0 0 0 40px;border:none;padding:0px"><div><div> Image frequency direction: normal</div></div></blockquote><blockquote style="margin:0 0 0 40px;border:none;padding:0px">
<div><div><font color="#ff0000"><img src="cid:B56@goomoji.gmail" style="margin: 0px 0.2ex; vertical-align: middle; " goomoji="B56">1) Using 3 cycles at lowest frequency to 16 at highest.</font></div></div></blockquote><blockquote style="margin:0 0 0 40px;border:none;padding:0px">
<div><div><font color="#ff0000"><img src="cid:B56@goomoji.gmail" style="margin: 0px 0.2ex; vertical-align: middle; " goomoji="B56">2) Generating 200 time points (-129.0 to 727.0 ms)</font></div></div></blockquote><blockquote style="margin:0 0 0 40px;border:none;padding:0px">
<div><div>Finding closest points for time variableTime values for time/freq decomposition is not perfectly uniformly distributed</div></div></blockquote><blockquote style="margin:0 0 0 40px;border:none;padding:0px"><div>
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<font color="#ff0000"><img src="cid:B56@goomoji.gmail" style="margin: 0px 0.2ex; vertical-align: middle; " goomoji="B56">3) The window size used is 71 samples (142 ms) wide.</font></div></div></blockquote><blockquote style="margin:0 0 0 40px;border:none;padding:0px">
<div><div><font color="#ff0000"><img src="cid:B56@goomoji.gmail" style="margin: 0px 0.2ex; vertical-align: middle; " goomoji="B56">4) Estimating 100 log-spaced frequencies from 23.4 Hz to 250.0 Hz.</font></div></div></blockquote>
<blockquote style="margin:0 0 0 40px;border:none;padding:0px"><div><div>Processing time point (of 200): 10 20 30 40 50 60 70 80 90 100 110 120</div></div></blockquote><blockquote style="margin:0 0 0 40px;border:none;padding:0px">
<div><div> 130 140 150 160 170 180 190 200</div></div></blockquote><blockquote style="margin:0 0 0 40px;border:none;padding:0px"><div><div>Computing the mean baseline spectrum</div></div></blockquote><blockquote style="margin:0 0 0 40px;border:none;padding:0px">
<div><div>Note: Add output variables to command line call in history to</div></div></blockquote><blockquote style="margin:0 0 0 40px;border:none;padding:0px"><div><div> retrieve results and use the tftopo function to replot them</div>
</div></blockquote></blockquote><div><div><div><br></div><div>Thanks in advance,</div><div><br></div><div>Zhu Mengyan</div><div><br></div><div><br></div>-- <br>Mengyan Zhu<br>Psychology department, Peking University<br>Dormitory 2061, Building 48,No.5 Yiheyuan Road, Haidian District, Beijing 100871, China <br>
E-mail: <a href="mailto:bj12116@gmail.com" target="_blank">bj12116@gmail.com</a><br>
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