[Eeglablist] Merging files for preprocessing and ICA

Wed Dec 7 06:16:09 PST 2016

Hey Makoto from UCSD J

Thanks for your very helpful response. That makes sense. I’m sorry, I forgot to cc eeglab list when I was writing back and forth with Tarik.

Related to the capping, I’m only performing one capping and my participants wear it throughout for task and nap, we also don’t do re-gelling. However, it is possible that during the nap the electrodes might slightly move and the position of the electrodes would slightly change. So I will not merge the files J And thanks, I feel honored that you adapted the preprocessing pipeline homepage for me ;-) Tarik and I were also discussing the possible influence of electrode impedance on ICA (e.g. over longer periods of time we see a clear reduction of impedance with our gel EGI nets), do you know anything about that?

Sankar and Sangtae say hello as well . Thanks again for a great and helpful eeglab course.

All the best,

Caroline

From: Makoto Miyakoshi [mailto:mmiyakoshi at ucsd.edu] 
Sent: Tuesday, December 06, 2016 11:16 PM
To: Caroline Lustenberger
Cc: EEGLAB List
Subject: Re: [Eeglablist] Merging files for preprocessing and ICA

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

_______________________________________________
Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
To unsubscribe, send an empty email to eeglablist-unsubscribe at sccn.ucsd.edu
For digest mode, send an email with the subject "set digest mime" to eeglablist-request at sccn.ucsd.edu

-- 

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

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://sccn.ucsd.edu/pipermail/eeglablist/attachments/20161207/afa97cf3/attachment.html>