[Eeglablist] Some confusion about ERSPs/ITC precomputing
bj12116 at gmail.com
Thu Jul 12 02:22:58 PDT 2012
Dear Makoto and eeglablists,
Thanks very much!
I have tried solution No.2, and it works well. I will try solution No.1
after I export longer epoch from BP analyzer since I'm interested in ERSPs
By the way, I have another question. I precomputed ERPs at baseline of -200
to 0ms. When I plot it, I added a high cut-off filter of 10Hz in parameter
settings.However, I found that the plot didn't at the baseline and I think
that is due to my 10Hz filter. Is that a bug? I just don't want to do the
filter before precomputing.....
Hope you could answer my question.
2012/7/11 Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
> Dear Mengyan,
> 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.
> 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!
> You can either
> 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.
> 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.
> You can try No.2 immediately. Use the following options to see what
> 'cycles', [2 0.5], 'freqs', [5 30], 'nfreqs', 50
> 2012/7/9 诸梦妍 <bj12116 at gmail.com>
>> Dear eeglablist,
>> 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.
>> 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.
>> 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.
>> 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 ( 1)
>> to 4) ) and hope that someone could tell me what should I do if I just
>> want it to compute 3Hz to 30Hz frequency.
>> Computing Event-Related Spectral Perturbation (ERSP) and
>> Inter-Trial Phase Coherence (ITC) images based on 31 trials
>> of 500 frames sampled at 500 Hz.
>> Each trial contains samples from -200 ms before to
>> 798 ms after the timelocking event.
>> Image frequency direction: normal
>> [?]1) Using 3 cycles at lowest frequency to 16 at highest.
>> [?]2) Generating 200 time points (-129.0 to 727.0 ms)
>> Finding closest points for time variableTime values for time/freq
>> decomposition is not perfectly uniformly distributed
>> [?]3) The window size used is 71 samples (142 ms) wide.
>> [?]4) Estimating 100 log-spaced frequencies from 23.4 Hz to 250.0 Hz.
>> Processing time point (of 200): 10 20 30 40 50 60 70 80 90 100 110 120
>> 130 140 150 160 170 180 190 200
>> Computing the mean baseline spectrum
>> Note: Add output variables to command line call in history to
>> retrieve results and use the tftopo function to replot them
>> Thanks in advance,
>> Zhu Mengyan
>> Mengyan Zhu
>> Psychology department, Peking University
>> Dormitory 2061, Building 48,No.5 Yiheyuan Road, Haidian District, Beijing
>> 100871, China
>> E-mail: bj12116 at gmail.com
>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
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> Makoto Miyakoshi
> JSPS Postdoctral Fellow for Research Abroad
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
Psychology department, Peking University
Dormitory 2061, Building 48,No.5 Yiheyuan Road, Haidian District, Beijing
E-mail: bj12116 at gmail.com
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