{
  "abstract": "Background/Objectives: EEG-based mental performance classification requires participant-separated validation and traceable feature identities. Methods: BioTWavePat-XFE constructs five single-rhythm and 15 signed cross-rhythm channel-rank transition layers, uses four cumulative-weighted selectors and tkNN/IMV classification, and decodes retained identities with Directed Lobish. We compared Protocol A (whole-dataset feature and pooled-test outcome selection), Protocol B (training-fold outcome selection with Protocol A features), and Protocol C (all data-dependent steps restricted to outer training data). STEW and MAT used participant-wise LOSO. TMPD included all 55 participants in 54 identifier groups; two same-class participants sharing an identifier were withheld together. Results: Protocol A segment accuracies were 83.08% (STEW), 93.14% (TMPD), and 91.15% (MAT). Protocol C accuracies were 80.46%, 82.50%, and 74.65%, with balanced accuracies of 80.46%, 75.17%, and 64.15%, respectively. These are the primary estimates without the identified selection leakage under the stated participant-separated validation design. Conclusions: Whole-dataset selection inflated the original estimates, particularly for TMPD and MAT. MAT Protocol C performance was close to majority prediction. Directed Lobish exposes selected feature identities without establishing physiological connectivity, causality or biomarker validity.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Digital Forensics Engineering, Technology Faculty, Firat University, 23119 Elazig, Türkiye",
    "Department of Computer Engineering, College of Engineering, Erzurum Technical University, 25050 Erzurum, Türkiye",
    "Department of Neurology, School of Medicine, Firat University, 23119 Elazig, Türkiye",
    "Department of Computer Technologies, Bingöl University, 12000 Bingöl, Türkiye",
    "Department of Computer Engineering, Faculty of Engineering, Munzur University, 62000 Tunceli, Türkiye",
    "Department of Electrical and Energy, Vocational School of Technical Sciences, Firat University, 23119 Elazig, Türkiye"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Tugce Keles"
    },
    {
      "affs": [
        1
      ],
      "name": "Mehmet Baygin",
      "orcid": "0000-0001-6449-8950"
    },
    {
      "affs": [
        2
      ],
      "name": "Irem Tasci"
    },
    {
      "affs": [
        3
      ],
      "name": "Erdem Gonultas",
      "orcid": "0009-0007-2238-8551"
    },
    {
      "affs": [
        4
      ],
      "name": "Taha Muezzinoglu",
      "orcid": "0000-0002-4551-3999"
    },
    {
      "affs": [
        5
      ],
      "name": "Burak Tasci",
      "orcid": "0000-0002-4490-0946"
    },
    {
      "affs": [
        0
      ],
      "name": "Sengul Dogan",
      "orcid": "0000-0001-9677-5684"
    },
    {
      "affs": [
        0
      ],
      "name": "Turker Tuncer",
      "orcid": "0000-0002-5126-6445"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4n63cn1zfnpsh.bib",
  "cited_by": 0,
  "doi": "10.3390/jcm15218471",
  "fulltext_url": null,
  "id": "4n63cn1zfnpsh",
  "issns": [
    "2077-0383"
  ],
  "landing_url": "https://www.mdpi.com/2077-0383/15/21/8471",
  "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": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/4n63cn1zfnpsh.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:21:07Z",
      "native_id": "10.3390/jcm15218471",
      "source": "crossref",
      "url": "https://www.mdpi.com/2077-0383/15/21/8471"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "journal-openalex:F27",
      "subfield": "general"
    }
  ],
  "title": "BioTWavePat: An EEG-Based Mental Performance Detection Model",
  "title_text": "BioTWavePat: An EEG-Based Mental Performance Detection Model",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T00:21:04Z",
  "url": "https://research.draconitic.com/paper/4n63cn1zfnpsh",
  "venue": "Journal of Clinical Medicine",
  "versions": []
}