[Eeglablist] Getting summary conclusion of band power analysis

[Simon-Shlomo Poil]

Dear Tommaso,

You can also use the NBT toolbox for this (an eeglab extension).

See
http://www.nbtwiki.net/doku.php?id=tutorial:amplitude_in_classical_frequency_bands#.UxAz_MEo80M

And
http://www.nbtwiki.net/doku.php?id=tutorial:compute_biomarkers#.UxA058Eo80M

Best wishes
Simon-Shlomo Poil, Dr.
 On Feb 28, 2014 6:56 AM, "Makoto Miyakoshi" <mmiyakoshi at ucsd.edu> wrote:

>  Dear Tommaso,
>
>  Sorry for delay.
> There is no dumb question. Your question is completely reasonable. The
> inconvenience you experienced is because EEGLAB does not allow users to
> access to basic metric through GUI*,* which is a legitimate problem.
>
>  For your request, please try the code below. If you want to calculate
> the ratio. add up all power for a denominator.
>
>  % for your epoched data, channel 1
> [spectra,freqs] = spectopo(EEG.data(2,:,:), 0, EEG.srate);
>
>  % delta=1-4, theta=4-8, alpha=8-13, beta=13-30, gamma=30-80
> deltaIdx = find(freqs>1 & freqs<4);
> thetaIdx = find(freqs>4 & freqs<8);
> alphaIdx = find(freqs>8 & freqs<13);
> betaIdx  = find(freqs>13 & freqs<30);
> gammaIdx = find(freqs>30 & freqs<80);
>
>  % compute absolute power
> deltaPower = 10^(mean(spectra(deltaIdx))/10);
> thetaPower = 10^(mean(spectra(thetaIdx))/10);
> alphaPower = 10^(mean(spectra(alphaIdx))/10);
> betaPower  = 10^(mean(spectra(betaIdx))/10);
> gammaPower = 10^(mean(spectra(gammaIdx))/10);
>
>  Makoto
>
>
> 2014-02-21 10:11 GMT-08:00 Tommaso Martino <tommartin at hotmail.it>:
>
>>   Dear all,
>> I'm new to EEGlab, and maybe this is a dumb question. By the way, let's
>> consider I have 2 seconds EEG registration for 1 electrode (the simplest
>> example).
>> I want to analyze it, saying, for example, that "alpha relative power is
>> 25%, beta relative power is 15%, theta relative power is 19%, etc..." (for
>> a total of ~100%).
>> As far as I now, this is the commonest method to summarize results of a
>> QEEG analysis in published studies (read those from Babiloni, for example).
>> Thanks for the answer!
>>
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>
>
>
>  --
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
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