[Eeglablist] A tentative issue in coherence calculation in EEGLAB for epoched data

[Makoto Miyakoshi]

Dear Iman,

If I understand the calculation correctly, you are comparing

mean(j1+j2+j3+j4+...+jn)

with

mean(mean(j1+j2)+mean(j3+j4)+...+mean(j(n-1)+jn))

They should produce the same results (if j is a data chunk, then they
should have the same number of data points... which holds anyway)

Do I understand the problem correctly?

Makoto

On Fri, Jul 24, 2015 at 10:43 AM, Iman Mohammad-Rezazadeh <
irezazadeh at ucdavis.edu> wrote:

>  Hi Makoto ,
>
> It is about the stationarity of EEG signal and basically we cannot assume
> it is stationary for the long period of the concatenated data.
>
> Best,
>
> Iman
>
>
>
> *From:* Makoto Miyakoshi [mailto:mmiyakoshi at ucsd.edu]
> *Sent:* Thursday, July 23, 2015 6:14 PM
>
> *To:* Iman Mohammad-Rezazadeh <irezazadeh at UCDAVIS.EDU>
> *Cc:* EEGLAB List <eeglablist at sccn.ucsd.edu>; Loo, Sandra <
> SLoo at mednet.ucla.edu>; Scott Makeig <smakeig at ucsd.edu>; Chantelle C
> Kinzel <ckinzel at mednet.ucla.edu>; Michelini, Giorgia <
> giorgia.michelini at kcl.ac.uk>
> *Subject:* Re: A tentative issue in coherence calculation in EEGLAB for
> epoched data
>
>
>
> Dear Iman,
>
>
>
> > coherres = sum(alltfX .* conj(alltfY), 3) ./ sqrt( sum(abs(alltfX).^2,3)
> .* sum(abs(alltfY).^2,3) );
>
>
>
> So 'sum(alltfX .* conj(alltfY), 3)' sums all data points? If so, as long
> as the number of data points distribute uniformly across all trials, the
> mean of the mean is the same as the grand mean. Am I wrong? Please correct
> me.
>
>
>
> Makoto
>
>
>
> On Thu, Jul 23, 2015 at 4:03 PM, Iman Mohammad-Rezazadeh <
> irezazadeh at ucdavis.edu> wrote:
>
>  Hi,
>
> The way the code scripted is to calculate the spectrum from the whole data
> at once
>
>
>
> [alltfX freqs timesout] = timefreq(X, g.srate, spectraloptions{:})
>
>
>
> Not each epoch separately and then make an average from them.  and yes in
> matters !
>
>
>
> Best,
>
> Iman
>
> *From:* Makoto Miyakoshi [mailto:mmiyakoshi at ucsd.edu]
> *Sent:* Thursday, July 23, 2015 3:44 PM
> *To:* Iman Mohammad-Rezazadeh <irezazadeh at UCDAVIS.EDU>
> *Cc:* EEGLAB List <eeglablist at sccn.ucsd.edu>; Loo, Sandra <
> SLoo at mednet.ucla.edu>; Scott Makeig <smakeig at ucsd.edu>; Chantelle C
> Kinzel <ckinzel at mednet.ucla.edu>; Michelini, Giorgia <
> giorgia.michelini at kcl.ac.uk>
> *Subject:* Re: A tentative issue in coherence calculation in EEGLAB for
> epoched data
>
>
>
> Dear Iman,
>
>
>
> I ask you this without testing it, but does the order of the averaging
> process makes difference in results?
>
>
>
> Makoto
>
>
>
> On Mon, Jul 20, 2015 at 10:09 AM, Iman Mohammad-Rezazadeh <
> irezazadeh at ucdavis.edu> wrote:
>
>
>
> Hi EEGLABERs,
>
> I have been looking into ‘newcrossf’ function and the way it calculates
> coherence for epoched data. Basically, it uses the ‘timefreq’ function to
> calculate the time/frequency decomposition the data.  ‘timefreq’ function
> treats the epoched data as a continuous one:
>
>
>
> X = reshape(X, g.frame, g.trials);
>
> [alltfX freqs timesout] = timefreq(X, g.srate, spectraloptions{:});
>
>
>
> Y = reshape(Y, g.frame, g.trials);
>
> [alltfY] = timefreq(Y, g.srate, spectraloptions{:});
>
>
>
> and calculates the its spectrum using the whole data which is now
> concatenated version of all trials.  So, for each of channel’s pair (X and
> Y , for example) the spectrum is calculated as described above and then the
> joint time-freq decomposition is calculated for coherence value.
>
>
>
> coherres = sum(alltfX .* conj(alltfY), 3) ./ sqrt( sum(abs(alltfX).^2,3)
> .* sum(abs(alltfY).^2,3) );
>
>
>
> *However, similar to the ERSP concept, each trial/epoch might be different
> than others [because of perturbations in subjects’ mental status, mental
> fatigue, etc] and thus I think it is more appropriate to calculate the
> coherence for each trial first and then make the average across trials.*
>
>
>
> Any thoughts?
>
> Iman
>
>
>
>
>
>
>
>
>
> --
>
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
>
>
>
>
> --
>
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>



-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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