{
  "abstract": "BackgroundIn cross-subject electroencephalography (EEG)-based driving-fatigue detection, most existing decoders are designed around second-scale short windows, whereas fatigue is a slow drift; short-window spectral features provide little direct evidence of that slow drift, limiting fatigue decisions. More critically, each individual’s spectral baseline level and fatigue-change pattern differ, so simply lengthening the window still leaves models struggling to predict across individuals from observed spectral features. This work aims to enable cross-subject fatigue decisions under shared parameters, without per-driver specialized models.MethodsWe propose early personal spectral reference with causal long-sequence residual encoding: an early personal spectral reference is estimated only from a short session-opening spectral window; subsequent spectral observations are mapped into residual space; state-conditioned temporal modeling accumulates and updates a residual state over a long causal sequence; and fatigue is read out from the terminal state.ResultsOn leave-one-subject-out binary classification of the Multimodal Phenotyping Dataset of Driving Fatigue (MPD-DF), five-seed (2026–2030) macro-averaged F1 is 0.655 ± 0.005 (0.660 at seed 2026), outperforming the best matched-protocol absolute-spectrum baseline (≈0.587). On continuous vigilance regression of the SEED Vigilance dataset (SEED-VIG) under a subject-LOSO protocol, root mean square error (RMSE) is 0.209 ± 0.005 under 5Bands and 0.200 ± 0.003 under 2 Hz, both below matched-protocol baselines.ConclusionAn early personal spectrum estimated from a short opening window can reduce interference from intrinsic individual spectral level in fatigue decisions and provides a practical premise for cross-subject fatigue decoding. Accumulating and updating residual state over a causal long sequence in residual space further improves cross-subject decisions. Under shared parameters without per-driver models, the framework outperforms matched absolute-spectrum baselines on both fatigue classification and vigilance regression.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Original Research",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Songdi Gao"
    },
    {
      "name": "Lanlan Lv"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2s72xq1xt2pmn.bib",
  "cited_by": 0,
  "doi": "10.3389/fnins.2026.1997832",
  "fulltext_url": null,
  "id": "2s72xq1xt2pmn",
  "issns": [
    "1662-453X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fnins.2026.1997832",
  "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": [
    [
      "feed-journal",
      "biology/neuroscience"
    ]
  ],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/2s72xq1xt2pmn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:18Z",
      "native_id": "10.3389/fnins.2026.1997832",
      "source": "feed-frontiers",
      "url": "https://www.frontiersin.org/articles/10.3389/fnins.2026.1997832"
    },
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      "first_seen": "2026-10-30T12:19:23Z",
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  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "feed-journal:biology/neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Cross-subject EEG driving fatigue detection: early personal spectral reference and causal long-sequence residual encoding",
  "title_text": "Cross-subject EEG driving fatigue detection: early personal spectral reference and causal long-sequence residual encoding",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T00:20:56Z",
  "url": "https://research.draconitic.com/paper/2s72xq1xt2pmn",
  "venue": "Frontiers in Neuroscience",
  "versions": []
}