Stress-Testing EEG Foundation Models for Clinical Decoding: Dataset Identity and Targeted Negative Controls
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
Record details
Published: 27 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Healthcare
Retrieved: 28 July 2026
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ethics.ai (27 July 2026), “Stress-Testing EEG Foundation Models for Clinical Decoding: Dataset Identity and Targeted Negative Controls,” evidence record 14045, https://ethics.ai/record/14045 (originally published by arXiv cs.AI).
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