[Eeglablist] Merging files for preprocessing and ICA

[Makoto Miyakoshi]

Dear Caroline,

Hi Caroline from North Carolina.

> I would like to hear your opinion about this idea, whether you also do
this approach and what I should consider when merging files.

That's not recommended. Do not concatenate differently capped datasets even
if they are recorded from a same subject. It's not the subject's anatomy,
but spatial relation between EEG sources and cap's electrodes is the issue.
This is what ICA models, therefore you can't perform a single ICA on a
differently capped subject. In other words, to apply an single ICA your
subject cannot take out the electrode cap. ICA's spatial filter is
surprisingly sensitive as you saw. With different capping, ICA wouldl
return split-half results i.e. one component explains only the first half
of the data, and the rest of the half is 'green' (i.e. zero variance
accounted for).

For the same reason, re-gelling during the break period is not recommended.

I updated my pipeline wiki page for you.
https://sccn.ucsd.edu/wiki/Makoto%27s_preprocessing_pipeline#Run_ICA_.2812.2F06.2F2016_updated.29

> however I clearly saw that many of the signals (e.g. muscle, but also
physiological brain components) changed in activation after the transition
of the merged files.

Right, this is what I mean.

> People are likely more relaxed after sleep, are maybe more focussed and I
was wondering whether merging of datasets is not ideal because conditions
are slightly different and I even expect that oscillatory activity relative
to the stimuli will slightly differ before and after sleep for the same
task. What is your impression?

In this particular case, for the abovementioned reason, this interpretation
seems incorrect. This is purely computational problem in ICA application.

Say hello to Sankara and Sangtae.

Makoto



On Wed, Nov 30, 2016 at 11:06 AM, Caroline Lustenberger <
lustenberger.caroline at gmail.com> wrote:

> Dear EEGLAB Team
>
>
>
> There is always the discussion about how many samples are needed for a
> good ICA and based on the tutorial pages from your lab it seems to be quite
> many. People that use high density EEG with 128 electrodes would likely
> need ~1000000 data points, which means more than 1 hour of EEG recording
> (considering we do downsample to 250 Hz). I doubt that all of us have
> recordings that are that long. At least I often have tasks that last for ~
> 10-20min. One idea that I thought of is to merge datasets of a person if
> she/he had multiple sessions on the same day with the same task (that is
> having the same net application). I would like to hear your opinion about
> this idea, whether you also do this approach and what I should consider
> when merging files.
>
>
>
> Here is a specific example: I tried this approach for a visual memory task
> during which my participants were seeing and rating pictures before a nap
> and after a nap. I merged the two datasets and after preprocessing (filter,
> clean_rawdata, interpolation, average referencing) I performed an ICA
> (tried both AMICA and RUNICA, results are very similar). The components
> seemed to be captured fine, however I clearly saw that many of the signals
> (e.g. muscle, but also physiological brain components) changed in
> activation after the transition of the merged files. More specific, the
> components were capturing the same type of activity (e.g. muscle,  eye
> blinks or alpha) but the amplitudes of the component clearly changed
> between the first and second data set. That is for instance one muscle
> component had strong activation in one dataset and was very low amplitude
> in the second part of the data. People are likely more relaxed after sleep,
> are maybe more focussed and I was wondering whether merging of datasets is
> not ideal because conditions are slightly different and I even expect that
> oscillatory activity relative to the stimuli will slightly differ before
> and after sleep for the same task. What is your impression? Would you
> rather process the files separately or merge them together to one?
>
>
>
> Thanks for your valuable input and best wishes,
>
> Caroline
>
>
>
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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