{
  "id": 4516,
  "url": "https://arxiv.org/abs/2605.11410v2",
  "title": "What Do EEG Foundation Models Capture from Human Brain Signals?",
  "summary": "Clinical electroencephalogram (EEG) analysis rests on a hand-crafted feature catalog refined over decades, \\emph{e.g.,} band power, connectivity, complexity, and more. Modern EEG foundation models bypass this catalog, learn directly from raw signals via self-supervised pretraining, and match or outperform feature-engineered baselines on most clinical benchmarks. Whether the two representations align is an open question, which we decompose into three sub-questions: \\emph{what does the model learn",
  "authors": "Ling Tang, Qian Chen, Jilin Mei, Houshi Xu, Quanshi Zhang, Jing Shao et al.",
  "category": "research",
  "topics": "healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-12T01:57:53.000Z",
  "fetched_at": "2026-07-14T16:31:03.579Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4516",
  "original_url": "https://arxiv.org/abs/2605.11410v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}