{
  "abstract": "Automated sleep staging is a fundamental application of passive Brain-Computer Interfaces (pBCI), decoding spontaneous neural states to enable closed-loop interventions independent of user intent. This study evaluates criticality features derived from Detrended Fluctuation Analysis (DFA) for the specific identification of deep sleep (N3). We analyzed $347,232$ EEG epochs from $290$ older women using UMAP manifold learning to visualize state transitions. Subsequently, six classifiers were benchmarked via 10-fold cross-validation, using balanced accuracy to determine the optimal \"state-sensing\" engine for neurofeedback.Naive Bayes achieved the highest mean balanced accuracy ($87.17\\% \\pm 0.24\\%$), significantly outperforming a fully connected deep neural network (FNN: $81.58\\%$) and Random Forest ($80.97\\%$). Linear models (LDA: $57.21\\%$; SVM: $51.01\\%$) performed poorly, indicating that DFA-derived criticality features reside on a distinct, non-linear manifold. Probabilistic decoding of EEG criticality provides a high-accuracy sensing mechanism for pBCIs. This robust classification pipeline supports the development of state-dependent neurofeedback, such as targeted auditory stimulation, to enhance cognitive recovery.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Proceedings of the10th Graz Brain-Computer Interface Conference 2026",
  "arxiv": "2606.13017",
  "author_total": 3,
  "authors": [
    {
      "name": "Stanisław Narębski"
    },
    {
      "name": "Tomasz Komendziński"
    },
    {
      "name": "Tomasz M. Rutkowski"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0vceyjyp691sk.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.13017",
  "fulltext_url": null,
  "id": "0vceyjyp691sk",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.13017",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "q-bio.NC"
    ],
    [
      "openalex",
      "2805|EEG and Brain-Computer Interfaces"
    ]
  ],
  "openalex": "W7164520069",
  "pdf_url": "https://arxiv.org/pdf/2606.13017",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0vceyjyp691sk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:59Z",
      "native_id": "2606.13017",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.13017"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "Deep Sleep Classification via EEG Signal Criticality: A Passive BCI Approach for Sleep-Improvement Neurofeedback",
  "title_text": "Deep Sleep Classification via EEG Signal Criticality: A Passive BCI Approach for Sleep-Improvement Neurofeedback",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:59Z",
  "url": "https://research.draconitic.com/paper/0vceyjyp691sk",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.3217/978-3-99161-093-9-066",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}