针对少量标注数据下的脑电模型适配难题，提出结构化原型引导方法。
SCOPE: Structured Prototype-Guided Adaptation for EEG Foundation Models with Limited Labels
- 构建群体级外部监督与置信度感知伪标签，指导有限标注下的学习
- 在50种设置下均显著提升性能，标签比例低至5%仍有效
- 适合脑电领域小样本迁移场景，尤其适用于预训练模型微调
脑电基础模型（EFM）在可迁移表征学习方面展现出强大潜力，但在仅有少量标注受试者的情况下，其实际应用仍面临挑战。我们发现，这一问题源于噪声大、样本少的监督信号与EFM高度可塑参数空间之间的结构不匹配，表现为三种关键失效模式：过度自信的校准偏差、预测坍缩和由无约束参数更新导致的表征漂移。为此，我们提出SCOPE——一种结构化置信度感知原型引导框架，用于标签受限的EFM适配。首先，构造群体级外部监督以提供持续引导，并生成置信度感知伪标签，筛选可靠未标注样本进行适配。在此基础上，引入ProAdapter，一种轻量级原型条件化适配器，通过调制冻结的EFM来保留预训练表征。在覆盖6个脑电任务、5种EFM骨干网络、以及5%-50%标注受试者比例的50种标签受限适配设置中，SCOPE始终表现优异且高效。
原文摘要 · Abstract (English)
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. To address these challenges, 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 for adaptation. Building on the constructed external supervision, SCOPE introduces ProAdapter, a lightweight prototype-conditioned adapter that modulates frozen EFMs to preserve pretrained representations. Experiments across 50 label-limited adaptation settings, covering 6 EEG tasks, 5 EFM backbones, and 5%-50% training labeled-subject ratios, show that SCOPE consistently achieves strong performance and efficiency.
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