Structured Prototype-Guided Adaptation for EEG Foundation Models

• URL: http://arxiv.org/abs/2602.17251v1
• Date: Thu, 19 Feb 2026 10:54:29 GMT
• Title: Structured Prototype-Guided Adaptation for EEG Foundation Models
• Abstract summary: We show that EEG foundation models exhibit poor generalization when subject-level supervision is limited.<n>This failure stems from a structural mismatch between noisy, limited supervision and the highly plastic parameter space of EFMs.<n>We propose SCOPE, a Structured COnfidence-aware Prototype-guided adaptation framework for EFM fine-tuning.
• Score: 56.35233377493694
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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