AI RESEARCH
Structured Prototype-Guided Adaptation for EEG Foundation Models
arXiv CS.LG
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ArXi:2602.17251v2 Announce Type: replace Electroencephalography (EEG) foundation models (EFMs) have shown strong potential for transferable representation learning, yet their adaptation in realistic settings remains challenging when only a few labeled subjects are available. We show that this challenge stems from a structural mismatch between noisy, limited supervision and the highly plastic parameter space of EFMs, reflected in three key failure modes: overconfident miscalibration, prediction collapse, and representation drift caused by unconstrained parameter updates.