[Eeglablist] Analysis of Frontal Alpha Asymmetry - am I on the right track?

Tarik S Bel-Bahar tarikbelbahar at gmail.com
Sun Jul 26 16:39:25 PDT 2020


https://urldefense.com/v3/__https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6449497/__;!!Mih3wA!VuktivcTazEYxkwB_zD1LqGHqEotYeiXcposSGzL2kdPuaohLWRtoj3FxjaaF9BmnmikKA$ 

On Sun, Jul 26, 2020, 11:58 AM Katarzyna Dudzikowska <
k.a.dudzikowska at gmail.com> wrote:

> Dear all,
>
> First of all, thank you for all the help I have gotten from this mailing
> list so far, I really appreciate it!
>
> For my MSc thesis I am trying to run a Frontal Alpha Asymmetry analysis.
> The end goal is an individual numeric score capturing interhemispheric
> difference in alpha activation for each of my subjects. I have extracted
> 1-sec-long epoch from each trial and I am going to derive the score from a
> dataset containing just these epochs. A typical dataset, in case it
> matters, has around 300 epochs, 250 frames per epoch (as sampling rate is
> 250Hz).
>
> Rather than use just one pair of electrodes, I want to use an aggregate of
> AF8, F6 and F8 for the right and AF7, F5 and F7 for the left hemisphere.
>
> Based on the previous literature, I want to run a fast Fourier transform
> analysis with a 50% Hamming window on the epoched dataset, extract alpha
> frequency power (8-13Hz), average across the right-side channels to obtain
> right alpha power and across the left-side channels to obtain left alpha
> power, log transform these values and subtract the left from the right to
> get my final score.
>
> This sounds simple enough, but I am not sure
> a) whether the code I have written definitely does the things I described
> above (I think so, but I would really love to get some feedback)
> b) whether before I log transform and subtract the values I should "compute
> absolute power" as described by Makoto here:
>
> https://sccn.ucsd.edu/wiki/Makoto's_useful_EEGLAB_code#How_to_extract_EEG_power_of_frequency_bands_.2806.2F06.2F2020_updated.29
> I have to admit that I do not understand why I would/wouldn't and what it
> does. I have searched the list and found some discussions you had on this
> topic, but I didn't understand much of it I'm afraid.
>
> Below is the code I compiled from literature + Makoto's advice I link
> above + this post:
> https://sccn.ucsd.edu/pipermail/eeglablist/2007/001903.html
> Does it make sense?
>
> %Define channels of interest:
> left_ind = [4 8 9]; %AF7, F7, F5
> right_ind = [7 15 16]; %AF8, F6, F8
>
> %Obtain power spectra at specified channels on the left and on the right
> [spectraL, freqs] = spectopo(EEG.data(left_ind,:,:), EEG.pnts, EEG.srate,
> 'winsize', EEG.srate, ...
>    'wintype', 'hamming', 'overlap', EEG.srate/2, 'plot', 'off');
>
> [spectraR] = spectopo(EEG.data(right_ind,:,:), EEG.pnts, EEG.srate,
> 'winsize', EEG.srate, ...
>     'wintype', 'hamming', 'overlap', EEG.srate/2, 'plot', 'off');
>
> % Aggregate of the chosen channels
> spectraLagg = mean(spectraL)
> spectraRagg = mean(spectraR)
>
>
> %Define alpha power as between 8 and 13Hz.
> alpha_ind = find(freqs>=8 & freqs<=13);
>
> % Here I am looking at two options and I have no idea which one to choose:
> alphaL = mean(spectraLagg(alpha_ind));
> alphaR = mean(spectraRagg(alpha_ind));
>
> FCA = log(alphaL) - log(alphaR)
>
> alphaLb = mean(10.^(spectraLagg(alpha_ind )/10));
> alphaRb = mean(10.^(spectraRagg(alpha_ind )/10));
>
> FCAb = log(alphaLb) - log(alphaRb)
>
> My most pertinent questions are:
>
> 1) Is it acceptable to run spectopo() only on selected channels or should I
> run it on the whole dataset and then extract the channels I am interested
> in? Does it make any difference?
>
> 2) Is it acceptable to simply average across my channels of interest to
> obtain a composite alpha power score, or should I employ some more
> sophisticated calculation?
>
> 3) *The most most-pertinent question*: should I go with FCAa or FCAb? For
> this dataset one ends up being a positive and the other a negative number,
> so this is a really important choice that I have no idea how to make. From
> what I understand it has to do with the values being expressed in either
> decibels or in uV^2, correct? Raw spectopo() output (is that dB?)? contains
> negative values which obviously mess things up for me when I average, so I
> am leaning towards option b, but the fact that similar transformation was
> not mentioned in any of the papers I looked at makes me doubt myself.
>
> 4) I also have a question regarding the spectopo() function. In the
> eeglablist post I link above, Arno calls the function with EEG.pnts as the
> second argument (frames per epoch). But in Makoto's example the value used
> is 0. Why would it ever be 0? Should it be 0 or EEG.pnts?
>
> I will be so incredibly grateful to get your input: for my first foray into
> the world of EEG analysis I picked a method that nobody I know has ever
> used, so I am completely relying on what I can find out myself and your
> kindness :)
>
> Best regards,
> Katia Dudzikowska
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