[Eeglablist] ASR with low density array

[Makoto Miyakoshi]

Dear Jonathan,

Sorry for belated response. Your questions are legitimate!

> First, we our montage is not high density – we only have 32 data
electrodes.

It does not matter.
'Artifact Subspace' in ASR means, if I understand correctly, principal
components associated with multiple channels (i.e spatial distribution of
PCs). The number of channels or their distributions does not matter.

> Second, our approach at the moment is to distribute them unevenly to
cover particular areas of interest – I will try to attach and image of the
montage.  I was wondering if people could weigh  in on the importance of
this to ASR.  For instance, might it be desirable to distribute the
electrodes evenly?

If you have a noisy channel and epoch in a scalp regions covered only
sparsely, then the 'artifact subspace' will include fewer number of
channels. That's it. It does not matter.

> Second, are there ballpark opinions about whether ASR should be used with
only 32 electrodes?  Just eyeballing it, the EEG looks reasonable with the
“burst” parameter set to 20, but some electrodes end up getting removed,
leaving us with even fewer electrodes as inputs to the algorithm.

The noisy channel removal is performed BEFORE ASR. So if you want to
protect channels, you should rather change the parameters for channel
rejections. ASR parameter has nothing to do with the final number of
channels you get!

> My worry is that this will result in more lossy corrections since ASR as
I understand it relies on correlations between electrodes.

In an extreme case, you can keep all channels and all datapoints while
cleaning data... but in this case what happens is that you'll get pretty
meaningless data in the places where the chunk of data should be usually
rejected by the program.

>From Lufthansa455 to Frankfurt,

Makoto



On Tue, Dec 20, 2016 at 7:30 AM, Jonathan Folstein <
jonathan.r.folstein at gmail.com> wrote:

> Dear eeglab, we are considering using artifact subspace reconstruction for
> offline artifact correction/rejection in a mobile EEG experiment. We are
> using the cognionics dry mobile EEG cap shown in this video
>
>
>
> https://www.youtube.com/watch?v=qYC_3SUxE-M
>
>
>
> ...but after having read a bit about ASR, there are some potentially
> relevant differences between our setup and other things that have been
> tried with ASR, e.g. here...
>
>
>
> https://sccn.ucsd.edu/wiki/Makoto's_preprocessing_pipeline
>
>
>
> First, we our montage is not high density – we only have 32 data
> electrodes. Second, our approach at the moment is to distribute them
> unevenly to cover particular areas of interest – I will try to attach and
> image of the montage.  I was wondering if people could weigh  in on the
> importance of this to ASR.  For instance, might it be desirable to
> distribute the electrodes evenly? Second, are there ballpark opinions about
> whether ASR should be used with only 32 electrodes?  Just eyeballing it,
> the EEG looks reasonable with the “burst” parameter set to 20, but some
> electrodes end up getting removed, leaving us with even fewer electrodes as
> inputs to the algorithm. My worry is that this will result in more lossy
> corrections since ASR as I understand it relies on correlations between
> electrodes.
>
>
>
> thanks, and happy holidays
>
>
>
> Jonathan
>
>
> --
> Jonathan Folstein
> Assistant Professor
> Department of Psychology
> Florida State University
> 1107 W. Call St.
> Tallahassee, FL 32306-4301
> office: 850-645-0654 <(850)%20645-0654>
> fax: 850-644-7739 <(850)%20644-7739>
> folstein at psy.fsu.edu
>
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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