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[2602.17251] Structured Prototype-Guided Adaptation for EEG Foundation Models
Summary
The paper presents SCOPE, a novel framework for fine-tuning EEG foundation models, addressing challenges in generalization under limited supervision through structured prototype-guided adaptation.
Why It Matters
This research is significant as it tackles the common issue of poor generalization in EEG models when subject-level supervision is limited, which is prevalent in clinical settings. By proposing a structured approach, it enhances the adaptability and performance of EEG models, potentially improving clinical outcomes.
Key Takeaways
- SCOPE framework improves EEG model adaptation under limited supervision.
- Two-stage pipeline: reliable external supervision and lightweight adaptation.
- Demonstrated strong performance across multiple EEG tasks and model backbones.
Computer Science > Machine Learning arXiv:2602.17251 (cs) [Submitted on 19 Feb 2026] Title:Structured Prototype-Guided Adaptation for EEG Foundation Models Authors:Jingying Ma, Feng Wu, Yucheng Xing, Qika Lin, Tianyu Liu, Chenyu Liu, Ziyu Jia, Mengling Feng View a PDF of the paper titled Structured Prototype-Guided Adaptation for EEG Foundation Models, by Jingying Ma and 7 other authors View PDF HTML (experimental) 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 thre...