[Eeglablist] microstate analysis

[Ramesh Srinivasan]

Kevin

Your description here nicely expressed my view of this for a number of
years.  However my views have  changed, and I believe this is a useful way
to think about the data and the brain.

In recent years a number of labs (including mine) have been interested in
deciding EEG at the.trial level to investigate perceptual and cognitive
states by making predictions.  This approach is a bit inspired by BCI folks
who face the challenge of operating devices from single trials of EEG.

I found intellectual appeal in this.  If you think about it neurons decide
the output of other neurons and EEG reflects this. So we should learn to do
this and maybe we get some insight on functional signals.

Recently I have come to appreciate that all of the people looking at trial
level EEG predictors are actually interested in microstates that encode
perceptual and cognitive states.  We just haven't been calling it that.
But what we are doing overlaps with that approach.


Ramesh Srinivasan
Professor
Cognitive Sciences
Biomedical Engineering

On Mon, May 6, 2024, 10:04 AM Kevin Spencer via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> Hello EEGers,
>
> Another couple of posts brought up the issue of microstate analysis, which
> has been bothering me recently. I'm not sure if I'm missing something, but
> the basic approach with ERPs, clustering topographies across the epoch into
> discrete "states", seems to ignore the well-established fact that the
> topography of the EEG across the scalp at a given time point represents the
> sum of numerous spatially and temporally overlapping activity patterns.
> That's why people originally applied PCA to ERP data, to disentangle these
> overlapping patterns. A classic example is that the "late positive complex"
> consists of several ERP components that overlap each other in time and
> space (e.g., P3a, P3b, classic Slow Wave, etc.).
>
> If you apply some type of cluster analysis to the topographies of epochs
> of EEG single trials or ERPs, you are going to get clusters that represent
> maxima in global field power, but which say nothing about the multiplicity
> of activity patterns that contribute to these clusters. In contrast, if you
> apply say ICA to the data, you decompose the data into distinct activity
> patterns that do overlap in various dimensions. So I don't see what is so
> useful about microstate analysis. But perhaps I'm missing something. I'd be
> interested in what people who know this method better than I do have to say.
>
> There is also the whole issue of what a brain "state" is. I'm not aware of
> any reason to think that a scalp topography that lasts for X ms constitutes
> a brain state. But again, maybe I'm not aware of research that supports
> this idea.
>
> Kevin
>
>
> ---------------------------------------------------------------------------------
> Kevin M. Spencer, Ph.D.
> Research Health Scientist, VA Boston Healthcare System
> Associate Professor of Psychiatry, Harvard Medical School
>
> ---------------------------------------------------------------------------------
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