[Eeglablist] microstate analysis

[Kevin Spencer]

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

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Kevin M. Spencer, Ph.D.
Research Health Scientist, VA Boston Healthcare System
Associate Professor of Psychiatry, Harvard Medical School
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