[Eeglablist] Video visualization EEG data

Makoto Miyakoshi mmiyakoshi at ucsd.edu
Mon Jan 27 18:11:54 PST 2014


Dear Jukka,

> On second thought it occurs to me that a video of a time-independent
construct like an ICA component wouldn't be too interesting.

Watch the movie 'Chapter 2 part 2' 12:21. You can see a very nice IC movies.
http://sccn.ucsd.edu/eeglab/Online_EEGLAB_Workshop/EEGLAB12_ica1.html

When I create this kind of movies I run a loop to visualize and save scalp
topos from which movies can be created using a free movie makers.

Sample code below. If you have a question ask me again.

**********************************
mapPeak = max(max(abs(EEG.data(:, start:start+600*samplesPerFrame,16))));
start = 69020-512*7; % sample
frameInterval = 33.33333;  % captured every 33 ms
unitTime = 1000/512; % 1 sample = 1.9531 ms
samplesPerFrame = round(frameInterval/unitTime); % 13 samples per a frame
for n = 1:600
    tmpSample = start+n*samplesPerFrame; % sample
    dataVector = EEG.data(:,tmpSample,16);
    h = figure; topoplot(dataVector, EEG.chanlocs, 'maplimits',
[-mapPeak*0.8 mapPeak*0.8]);
    title([num2str(round((tmpSample-start)*unitTime)) 'ms'], 'FontSize', 20)

    print('-djpeg90', sprintf('%03d', n))

    close(h)
end
*************************************

Makoto

2014-01-26 Jukka Zitting <jukka.zitting at gmail.com>

> Hi,
>
> On Sun, Jan 26, 2014 at 1:45 PM, Jukka Zitting <jukka.zitting at gmail.com>
> wrote:
> > I'm looking for a way to visualize selected aspects of continuous EEG
> > data, for example by creating a video of the topoplot() output over
> > time for selected frequency bands or ICA components.
>
> On second thought it occurs to me that a video of a time-independent
> construct like an ICA component wouldn't be too interesting. Anyway,
> to give a bit more background, we're interested in spotting features
> like delta or alpha waves while a subject is falling asleep. A video
> visualization (besides being a nice demo feature :-) could help
> identify interesting periods of activity that might otherwise be hard
> to spot or auto-detect within the continuous raw data.
>
> BR,
>
> Jukka Zitting
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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