{
  "abstract": "Electroencephalography (EEG) microstate analysis commonly converts each scalp topography into a winner-take-all hard label and summarises the resulting sequence using duration, occurrence, coverage, transitions, and symbolic complexity. Although interpretable, this readout discards evidence strength, assignment ambiguity, and low-confidence periods. We introduce a template evidence trajectory framework that retains, at each sampled Global Field Power (GFP) peak, the evidence for all templates or subject-specific topographic communities. Conventional hard labels are treated as a compressed readout of this multivariate trajectory. We derive two evidence-aware extensions of classical descriptors: high-evidence episode duration and episode rate, which quantify temporal clustering and fragmentation of strong state evidence, and null-state Lempel-Ziv complexity (LZC), which explicitly encodes insufficient-evidence periods. We evaluated the framework across four resting-state EEG datasets spanning Alzheimer's disease and age-related variation. Fixed-state models included K-means, AAHC, and HMMs at K = 4 and K = 7, together with adaptive subject-specific Leiden and Infomap communities. Across 32 dataset-model cases, trajectory-derived duration effects exceeded matched hard-label effects in all cases at the representative setting and in 30-32 cases across a broader parameter grid. Null-state LZC improved over traditional LZC in most cases, while classification showed modest but consistent gains for trajectory or combined features. Retaining template evidence therefore provides a more sensitive readout of EEG topographic state dynamics while remaining compatible with conventional microstate analysis.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.08500",
  "author_total": 5,
  "authors": [
    {
      "name": "Kaidong Wu"
    },
    {
      "name": "Haili Ye"
    },
    {
      "name": "Ptolemaios G Sarrigiannis"
    },
    {
      "name": "Daniel J Blackburn"
    },
    {
      "name": "Fei He"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/chxnd3frdy1nz.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.08500",
  "fulltext_url": null,
  "id": "chxnd3frdy1nz",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.08500",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-bio.NC"
    ],
    [
      "openalex",
      "2805|EEG and Brain-Computer Interfaces"
    ]
  ],
  "openalex": "W7211993065",
  "pdf_url": "https://arxiv.org/pdf/2609.08500",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/chxnd3frdy1nz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:02Z",
      "native_id": "2609.08500",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.08500"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "An Evidence-Aware Framework for EEG Microstate Analysis: Improved Sensitivity to Alzheimer's Disease and Ageing",
  "title_text": "An Evidence-Aware Framework for EEG Microstate Analysis: Improved Sensitivity to Alzheimer's Disease and Ageing",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:02Z",
  "url": "https://research.draconitic.com/paper/chxnd3frdy1nz",
  "venue": "arXiv",
  "versions": []
}