Cross-subject EEG generalization is challenging due to substantial heterogeneity across subjects. Existing methods typically learn either a shared subject-invariant model or multiple subject-specialized experts, but these two paradigms fail in complementary ways: the former may over-reduce subject-specific discriminative signals, while the latter may under-reduce transferable structure. We show that their suitability depends on the reducibility cost of branch-specific functions to branch-invariant ones, and we further provide a theory-to-method mapping that instantiates alignment principles in cross-subject EEG learning. Based on this insight, we propose Shared-Routed Expert Alignment (SREA), a collaborative framework that couples a shared expert for reducible invariant functions with routed experts for irreducible subject-specific functions. SREA trains the shared branch with joint embedding over augmented temporal neighbors, the routed branch with prototype-based sparse routing and expert specialization, and both branches with numerically stable mutual-guided reweighting based on cross-branch learnability gaps. Experiments on seven public EEG benchmarks across different tasks show that SREA consistently outperforms state-of-the-art methods and EEG foundation models.

翻译：跨被试脑电图（EEG）泛化因被试间存在显著异质性而极具挑战。现有方法通常学习一个共享的被试不变模型或多个被试专用专家，但这两种范式在互补方式上存在缺陷：前者可能过度削弱被试特异性判别信号，而后者可能不足够保留可迁移结构。我们证明，其适用性取决于分支特异性函数向分支不变函数转化的可约性代价，并进一步提供了一套从理论到方法的映射，该映射在跨被试脑电图学习中实例化了对齐原则。基于这一洞察，我们提出共享-路由专家对齐（SREA），一种协同框架，该框架耦合了一个用于可约不变函数的共享专家与多个用于不可约被试特异性函数的路由专家。SREA通过增广时间邻域的联合嵌入训练共享分支，通过基于原型的稀疏路由与专家特化训练路由分支，并基于跨分支可学习性差距并通过数值稳定的互导加权训练两个分支。在七个涵盖不同任务的公开脑电图基准上的实验表明，SREA一致优于最先进的方法及脑电图基础模型。