Abstract
In the resting state, the human brain quickly transitions among a limited set of quasi-stable states known as EEG microstates, which
characterize brain spatio-temporal activity with high temporal resolution. EEG microstates are typically identified using a clustering
algorithm that analyzes peaks in the EEG global field power. In this study, we focus on the modified k-means algorithm (modKM), one of the
most commonly employed methods for detecting EEG microstates. We demonstrate, both theoretically and through simulation and real EEG data,
its equivalence with a different method known as the Implicit Slice Canonical Decomposition (IMSCAND), which is a special kind of tensor
decomposition for symmetric higher order arrays. This relationship opens new avenues for EEG microstate detection, interpretation, and
analysis by utilizing tensor decomposition methods and related techniques.
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