mstsa: A Python Library for Information-Theoretic and Statistical Analysis of EEG Microstate Sequences
Year: 2026
Type: preprint
Abstract: EEG microstate analysis decomposes a multichannel EEG recording into a discrete symbolic time series: each sample is assigned to one of a small number of voltage topography templates, the microstate maps, typically at sampling rates of 100–500 Hz (Lehmann et al., 1998; Michel & Koenig, 2018; Pascual-Marqui et al., 1995). Back-fitting the templates into the original EEG dataset yields a symbolic time series, the microstate sequence. Many research questions address the temporal structure of these sequences, such as: time series memory (Markovianity), autocorrelations including persistence and long-range correlations, measures of syntax and complexity, and comparison against certain null models.mstsa (Microstate Time Series Analysis) is a Python library that addresses these questions. It provides a comprehensive set of information-theoretic, Markov-chain, and statistical tools for the analysis of discrete symbolic sequences, with EEG microstate data as the primary application.
Source: PsyArXiv (OSF Preprints)
Author: FREDERIC VON WEGNER
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7212324287