SCOPE: Structured Prototype-Guided Adaptation for EEG Foundation Models with Limited Labels

Electroencephalography foundation models (EFMs) have shown strong potential for transferable representation learning, but their real-world adaptation remains challenging when only a few labeled subjects are available. We trace this challenge to a structural mismatch between noisy, limited supervision and the highly plastic parameter space of EFMs, reflected in three key failure modes: overconfident miscalibration, transient optimization collapse, and disruption of pretrained representations under unconstrained updates. To address this mismatch, we propose SCOPE, a Structured COnfidence-aware Prototype-guided framework for label-limited EFM adaptation. SCOPE first constructs cohort-level external supervision to provide persistent guidance and further derives confidence-aware pseudo-labels to select reliable unlabeled samples. Building on this supervision, SCOPE introduces ProAdapter, a lightweight prototype-conditioned adapter that modulates frozen EFMs to preserve pretrained representations. We comprehensively evaluate SCOPE across 60 adaptation settings spanning 6 tasks, 5 EFM backbones, and 5%-100% labeled subject ratios. In label-limited settings, SCOPE outperforms full fine-tuning by 4.6 multiclass Kappa and 4.4 binary PRAUC points on average.