{
  "abstract": "Introduction: Before asking which microstate an EEG sample belongs to, there is a simpler question: can it look like a microstate at all? Yet a winning correlation mixes three different properties: how microstate-like the scalp field is, where it lies within that geometry, and how well the available templates represent that direction. We asked whether a fixed geometry, independent of any microstate solution, could explain common structure of microstate maps, constrain how strongly EEG patterns can match them, and describe that structure in interpretable terms. Methods: We introduce First-Order spherical haRMonic (FORM), projecting normalized scalp topographies onto a fixed three-dimensional subspace defined only by sensor geometry. Direction describes field geometry; projection norm, {rho}, measures FORM conformity and the maximum correlation attainable by any FORM topography. We tested simulations, literature templates, and 374 recordings from 187 LEMON participants. Results: FORM captured canonical microstate geometry despite being defined a priori of a microstate solution: FORM explained energy, {rho} squared, was 0.990-0.998 for five meta-microstates and 0.966-0.986 for pooled LEMON maps. Across 313 literature templates, FORM angular distances preserved their independently derived organization (Spearman {rho}=0.987). In continuous EEG, FORM conformity explained a median 74.4% of within-subject variance in winning-template correlation, versus 21.7% for GFP. As independently estimated dictionaries increased from k=1 to k=12, the median fitted fraction of the FORM ceiling rose from 0.613 to 0.970. Discussion: FORM places an a priori continuous geometry beneath discrete microstate labels. It separates properties intrinsic to the scalp field - whether it is microstate-like and where it lies within that geometry - from how well the chosen template dictionary represents it. FORM therefore provides an interpretable, clustering-independent reference for assignment strength, template comparison and continuous microstate dynamics.",
  "abstract_shown": true,
  "affiliations": [
    "IRCCS Fondazione Don Carlo Gnocchi ETS"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "L. Corsi"
    },
    {
      "name": "P. Liuzzi"
    },
    {
      "name": "C. M. Oddo"
    },
    {
      "name": "A. Mannini"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cj8w79nqarcee.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.24.754021",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.24.754021v1.full",
  "id": "cj8w79nqarcee",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.24.754021v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "neuroscience"
    ],
    [
      "openalex",
      "2805|EEG and Brain-Computer Interfaces"
    ]
  ],
  "openalex": "W7214759212",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.24.754021v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cj8w79nqarcee.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:15Z",
      "native_id": "10.64898/2026.09.24.754021",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.24.754021v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Resting state EEG microstates: is a new FORM all you need?",
  "title_text": "Resting state EEG microstates: is a new FORM all you need?",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:15Z",
  "url": "https://research.draconitic.com/paper/cj8w79nqarcee",
  "venue": "bioRxiv",
  "versions": []
}