{
  "id": 14045,
  "url": "https://arxiv.org/abs/2607.24519v1",
  "title": "Stress-Testing EEG Foundation Models for Clinical Decoding: Dataset Identity and Targeted Negative Controls",
  "summary": "Pretrained EEG foundation models are increasingly proposed for clinical decoding, but their transfer across populations and robustness to negative controls remain unclear. We benchmark six models (LaBraM, EEGMamba, CBraMod, REVE, BENDR, and BIOT) on five clinical tasks across four datasets using frozen linear probes with leave-one-subject-out, subject-grouped, or explicitly identified recording-level splits. Selected REVE findings are tested against random initialisation, random features, label",
  "authors": "Marzieh Zare",
  "category": "research",
  "topics": "healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-27T14:59:42.000Z",
  "fetched_at": "2026-07-28T05:10:12.325Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/14045",
  "original_url": "https://arxiv.org/abs/2607.24519v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}