[Eeglablist] Problems in calculating baseline power estimates of tfdata

[Arnaud Delorme]

Dear Cindy,

in newtimef the log is performed after trial averaging. One of the reasons is because the log function can blow up some very small values.

Best,

Arno

On 22 Mar 2013, at 18:09, Makoto Miyakoshi wrote:

> Dear Cindy,
> 
> I'm interested in knowing how they are different. Do they differ only by DC shift? Do they differ completely with nothing in common? Why don't you send us some screencaptures of your data visualization (using imagesc...)?
> 
> Makoto
> 
> 2013/3/20 Cindy Eckart <cindy.eckart at web.de>
> Dear EEGLablist-experts,
> 
> I am working on a time-frequency analysis for EEG-Data of a working memory task and thus have to correct a retention phase (the time period where some images have to be memorized) to an 'external' baseline (fixation cross before the first image is presented). However, I am uncertain if I procede correctly. 
> 
> To get the standard deviation of baseline trials I saved the single trials (tfdata) for each subject. As tfdata consists of complex data I tried to transform it to power information with ERSP{n}.tfdataPOW{r} = 10*(log10(abs(tfdata).^2)) and saved it in a frequency x timepoint x trials - matrix tfdataPOW, where n is the condition (baseline) and r the channel. 
> 
> Well, if I understood (and did) everything correctly the mean over all trials (i.e. mean(ERSP{1,1}.tfdataPOW{1,1},3)) should now correspond to the power estimates of newtimef (i.e. ERSP{1,1}.chan{1,1}(1,:)); however, this is not the case.... Do I transform the data in a wrong way??
> 
> Thank you very much for your help!
> 
> Kind regards, 
> 
> Cindy
> 
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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
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