{
  "abstract": "Resting-state electroencephalography (EEG) can capture the slowing of neural oscillations associated with Alzheimers disease (AD), but many machine-learning studies remain difficult to inspect, reproduce, or test. This study developed an interpretable, subject-level AD versus healthy-control classifier from the dataset OpenNeuro ds004504 and deployed it as a public research prototype. Preprocessed eyes-closed EEG recordings from 36 people with AD and 29 healthy controls were analysed. Welch power spectral density estimates were used to generate absolute and relative bandpower summaries, theta/alpha and delta/alpha ratios, a slow/fast ratio, and signal-level descriptors. Five-fold stratified cross-validation compared two Random Forest configurations, EEG Logistic Regression, an age-only baseline, and an EEG-plus-age model. EEG Logistic Regression gave the strongest mean fold-wise performance: accuracy 0.846, balanced accuracy 0.848, F1 score 0.849, and ROC-AUC 0.948. Age alone was near chance, while adding age did not improve the EEG-only model. The feature pattern was consistent with AD-related EEG slowing, including higher slow-wave-related ratios and lower relative alpha power. The trained pipeline was deployed as a Gradio application on Hugging Face Spaces. The result is a reproducible research and educational prototype, not a clinical diagnostic device, and requires external validation before any clinical interpretation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "A. DAS"
    },
    {
      "name": "A. Chakravarty"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5t3gm4d7mwrzw.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.12.751104",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.12.751104v1.full",
  "id": "5t3gm4d7mwrzw",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.12.751104v1",
  "language": null,
  "licence": "CC-BY-ND",
  "licence_label": "CC BY-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "neuroscience"
    ],
    [
      "openalex",
      "2805|EEG and Brain-Computer Interfaces"
    ]
  ],
  "openalex": "W7213560340",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.12.751104v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5t3gm4d7mwrzw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:06Z",
      "native_id": "10.64898/2026.09.12.751104",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.12.751104v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "An Interpretable EEG Machine Learning Prototype for Alzheimers Disease Classification",
  "title_text": "An Interpretable EEG Machine Learning Prototype for Alzheimers Disease Classification",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:06Z",
  "url": "https://research.draconitic.com/paper/5t3gm4d7mwrzw",
  "venue": "bioRxiv",
  "versions": []
}