mstsa: A Python Library for Information-Theoretic and Statistical Analysis of EEG Microstate Sequences
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.
Versions
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BibTeX
@misc{wegner2026mstsa,
title = {{mstsa: A Python Library for Information-Theoretic and Statistical Analysis of EEG Microstate Sequences}},
author = {FREDERIC VON WEGNER},
howpublished = {PsyArXiv},
year = {2026},
month = sep,
doi = {10.31234/osf.io/cwgnj_v1},
url = {https://osf.io/preprints/psyarxiv/cwgnj_v1/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - mstsa: A Python Library for Information-Theoretic and Statistical Analysis of EEG Microstate Sequences
AU - FREDERIC VON WEGNER
PY - 2026
DA - 2026/09/10
PB - PsyArXiv
DO - 10.31234/osf.io/cwgnj_v1
UR - https://osf.io/preprints/psyarxiv/cwgnj_v1/
AB - 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.
ER -