Dear Mengyan,<div><br></div><div>If you want to have 3 Hz to 30 Hz range after wavelet transform, the problem is that you have only -200 to 800 ms so that it is extremely difficult to have 3 Hz.</div><div><br></div><div>If you use 3 cycles at 3 Hz, your window side is (1000ms/3Hz)*3cycles = 1000ms, meaning that 1000ms length data generates 1 datapoint after wavelet transform. So your epoch can allow to generate only 1 datapoint!</div>
<div><br></div><div>You can either</div><div><br></div><div>1. prepare longer epoch (from -1000 ms to 2000 ms relative to stimulus onset) and use 3 Hz 3 cycle at the lowest: then you'll have -500 to 1500 ms after wavelet transform. You lose 500 ms from both sides of your epoch, because your window side is 1000 ms. It does not matter very much if your epoch overlaps each other if it is not too much.</div>
<div><br></div><div>2. Give up the lowest frequency range of 3 Hz and use 5 Hz instead with 2 cycles (decreasing the cycle number reduces sensitivity though). Then you'll have 0 to 800 ms after wavelet transform. You lose 200 ms from both sides of your epoch since your window size is (1000ms/5Hz)*2cycles = 400ms.</div>
<div><br></div><div>You can try No.2 immediately. Use the following options to see what happens.</div><div><br></div><div>'cycles', [2 0.5], 'freqs', [5 30], 'nfreqs', 50</div><div><br></div><div>
Makoto</div>
<div><br></div><div><br></div><div><br><div class="gmail_quote">2012/7/9 诸梦妍 <span dir="ltr"><<a href="mailto:bj12116@gmail.com" target="_blank">bj12116@gmail.com</a>></span><br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
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 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 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>
<div>
<font color="#ff0000"><img 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 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><span class="HOEnZb"><font color="#888888"><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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-- <br>Makoto Miyakoshi<br>JSPS Postdoctral Fellow for Research Abroad<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<br><br>
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