{
  "abstract": "Sleep stage classification from electrophysiological signals is essential for studying brain function in preclinical neuroscience, yet models often fail to generalize across subjects and experimental conditions, particularly under pharmacological interventions that alter neural and muscular activity. We propose a robustness-oriented deep learning framework that integrates multi-branch feature extraction, global contextual representation, sequence modeling, and training strategies designed to reduce sensitivity to subject- and treatment-specific variability. The approach is evaluated on a large rodent dataset comprising over one million annotated EEG and EMG segments collected from multiple animals across several pharmacological conditions. It achieves strong classification performance, substantially outperforming both classical methods and standard deep learning baselines. Notably, the model maintains stable performance across treatments, showing minimal degradation when trained on limited conditions, and remains effective even with relatively small amounts of annotated data. These findings demonstrate that accurate and robust sleep stage classification is achievable despite pronounced experimental variability. The proposed framework reduces reliance on extensive annotation and improves the reliability of automated sleep analysis, supporting more scalable and reproducible preclinical research.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Vojtěch Brejtr"
    },
    {
      "name": "Kristýna Aleksić"
    },
    {
      "name": "Klára Šíchová"
    },
    {
      "name": "Čestmír Vejmola"
    },
    {
      "name": "Tomáš Páleníček"
    },
    {
      "name": "Lenka Lhotská"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/53bmdpa8f41cq.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-57917-0",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-57917-0",
  "id": "53bmdpa8f41cq",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-57917-0",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0",
  "metadata_licence": null,
  "native_subjects": [
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  "pdf_url": "https://www.nature.com/articles/s41598-026-57917-0.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/53bmdpa8f41cq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:31Z",
      "native_id": "10.1038/s41598-026-57917-0",
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      "url": "https://www.nature.com/articles/s41598-026-57917-0"
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    {
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      "url": "https://doi.org/10.1038/s41598-026-57917-0"
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  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2805|EEG and Brain-Computer Interfaces",
      "subfield": "neuroscience"
    }
  ],
  "title": "Global context integration for pharmacologically robust preclinical sleep stage classification",
  "title_text": "Global context integration for pharmacologically robust preclinical sleep stage classification",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:59Z",
  "url": "https://research.draconitic.com/paper/53bmdpa8f41cq",
  "venue": "Scientific Reports",
  "versions": []
}