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.

翻译：脑电图基础模型在全参数微调下已取得优异性能，但在受试者级监督有限时表现出较差的泛化能力，这是真实临床场景中的常见约束。我们证明这种失效不仅源于有限监督，更源于噪声性有限监督与脑电图基础模型高可塑性参数空间之间的结构失配。为解决这一挑战，我们提出SCOPE——一种面向脑电图基础模型微调的结构化置信感知原型引导自适应框架。SCOPE采用两阶段流程：第一阶段通过学习几何正则化的任务先验，在所得嵌入空间构建平衡的类级别原型，并根据其一致性生成置信感知伪标签，从而过滤未标记数据中的不可靠信号，构建可靠的外部监督；第二阶段引入ProAdapter，该模块通过基于结构化原型的轻量级适配器来调整冻结的脑电图基础模型。在三个脑电图任务和五种基础模型骨干上的实验表明，SCOPE在标签受限的跨受试者设置下始终能实现优异的性能与效率。