Structured Prototype-Guided Adaptation for EEG Foundation Models
This addresses a practical limitation in deploying EEG AI models for clinical applications where labeled data is scarce, representing an incremental improvement in adaptation methods for foundation models.
The paper tackles the problem of EEG foundation models performing poorly with limited subject-level supervision in clinical settings by proposing SCOPE, a two-stage adaptation framework that uses structured prototypes and confidence-aware pseudo-labels, achieving strong performance across three EEG tasks and five foundation model backbones under label-limited conditions.
Electroencephalography (EEG) foundation models (EFMs) have achieved strong performance under full fine-tuning but exhibit poor generalization when subject-level supervision is limited, a common constraint in real-world clinical settings. We show that this failure stems not merely from limited supervision, but from a structural mismatch between noisy, limited supervision and the highly plastic parameter space of EFMs. To address this challenge, we propose SCOPE, a Structured COnfidence-aware Prototype-guided adaptation framework for EFM fine-tuning. SCOPE follows a two-stage pipeline. In the first stage, we construct reliable external supervision by learning geometry-regularized task priors, constructing balanced class-level prototypes over the resulting embeddings, and producing confidence-aware pseudo-labels from their agreement to filter unreliable signals on unlabeled data. In the second stage, we introduce ProAdapter, which adapts frozen EEG foundation models via a lightweight adapter conditioned on the structured prototypes. Experiments across three EEG tasks and five foundation model backbones demonstrate that SCOPE consistently achieves strong performance and efficiency under label-limited cross-subject settings.