[Eeglablist] Enquiry regarding power values calculated using spectopo

[Cedric Cannard]

Hi,

There are probably diferent ways to do it.
I personally do it like this for example for continuous data over channel 1:

1) spectra = spectopo(EEG.data(1,:), EEG.pnts, EEG.srate, 'plot','off');
2) spectra = 10.^spectra; %brings log dB signal to power (amplitude = power squared)
3) compute mean, SD or whatever you want here on power values
4) spectra = log10(spectra)  %bring back to log for more normalized signal across subjects

Best,
Cedric


--
Cédric Cannard, MSc, PhD candidate in Neuroscience
Université Toulouse III, CerCo Lab, CNRS, France
ccannard at protonmail.com

‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐
On Saturday, May 2, 2020 6:12 AM, #HENG JIAMIN GLADYS# <JHENG007 at e.ntu.edu.sg> wrote:

> Dear EEGlab list,
>
> I have calculated power values using the following formula, as found in a previous post (https://sccn.ucsd.edu/pipermail/eeglablist/2015/009249.html):
>
> % for your epoched data, channel 2
> [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 = mean(10.^(spectra(deltaIdx)/10));
> thetaPower = mean(10.^(spectra(thetaIdx)/10));
> alphaPower = mean(10.^(spectra(alphaIdx)/10));
> betaPower = mean(10.^(spectra(betaIdx)/10));
> gammaPower = mean(10.^(spectra(gammaIdx)/10));
>
> From my understanding, spectopo "plots the mean log spectrum of a set of data epochs at all channels as a bundle of traces".
> Therefore, the spectra output is mean log values.
> With the formula to compute absolute by taking 10.^(spectra(freqIdx)/10), does that mean that the power value is transformed back to actual raw values?
>
> I'm asking this as my resulting data appear to be mostly positively skewed, and I am planning to do a log10 transformation, but am not sure if I am applying log transformation twice on the data.
>
> Thanks in advance,
> Gladys
>
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