{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "FREDERIC VON WEGNER"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2h7d06aw91xjz.bib",
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  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Computational Neuroscience"
    ],
    [
      "bepress",
      "Neuroscience"
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    [
      "openalex",
      "2805|EEG and Brain-Computer Interfaces"
    ]
  ],
  "openalex": "W7212324287",
  "pdf_url": "https://osf.io/cwgnj/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2h7d06aw91xjz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:30Z",
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      "url": "https://osf.io/preprints/psyarxiv/cwgnj_v1/"
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  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "bepress:Neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "mstsa: A Python Library for Information-Theoretic and Statistical Analysis of EEG Microstate Sequences",
  "title_text": "mstsa: A Python Library for Information-Theoretic and Statistical Analysis of EEG Microstate Sequences",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:30Z",
  "url": "https://research.draconitic.com/paper/2h7d06aw91xjz",
  "venue": "PsyArXiv",
  "versions": [
    {
      "doi": "10.5281/zenodo.22181409",
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}