[Eeglablist] microstate analysis

[Makoto Miyakoshi]

Hi Kevin,

I mostly agree with you. Here are my two yen.
Without doing the microstate analysis myself, after reading several papers
on this approach, I thought that the 'microstate analysis' is a spatial
mode analysis with unfiltered data. 'Unfiltered' means the signal 'uses'
the default forward model, which is described in EFB by Nunez and
Srinivasan. Because our scalp EEG data are amplitude-wise dominated by
alpha rhythms that have broadly distributed sources, the results from the
microstate analysis are primarily dominated by the alpha dynamics. One of
the papers I read reported they often find 4 dominant states which
take turns in a certain interval. I found this interval was the inverse of
the alpha frequency.

I'd love to know if there is something more than that in the microstate
analysis.

Makoto

On Mon, May 6, 2024 at 1:10 PM 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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