Decoding the human brain from electroencephalography (EEG) signals holds promise for understanding neurological activities. However, EEG data exhibit heterogeneity across subjects and sessions, limiting the generalization of existing methods. Representation learning approaches sacrifice subject-specific information for domain invariance, while ensemble learning methods risk error accumulation for unseen subjects. From a theoretical perspective, we reveal that the applicability of these paradigms depends on the reducibility cost of domain-specific functions to domain-invariant ones. Building on this insight, we propose a Mutual-Guided Expert Collaboration (MGEC) framework that employs distinct network structures aligned with domain-specific and domain-invariant functions. Shared expert-guided learning captures reducible domain-invariant functions. Routed expert-guided learning employs a mixture-of-experts architecture to model irreducible domain-specific functions. Mutual-guided learning enables collaborative regularization to prevent over-reduction and under-reduction. We validate our theoretical findings on synthetic datasets, and experiments on seven benchmarks demonstrate that MGEC outperforms state-of-the-art methods.

翻译：从脑电图信号解码人类大脑活动为理解神经活动提供了前景。然而，脑电数据在受试者与会话间存在异质性，限制了现有方法的泛化能力。表征学习方法为获得域不变性而牺牲了被试特异性信息，而集成学习方法对未见受试者存在误差累积风险。从理论视角出发，我们揭示了这些范式的适用性取决于领域特定函数向域不变函数归约的成本。基于这一洞见，我们提出了一种互导专家协作框架，该框架采用与领域特定函数和域不变函数对齐的不同网络结构。共享专家引导学习捕获可归约的域不变函数。路由专家引导学习采用专家混合架构来建模不可归约的领域特定函数。互导学习通过协作正则化防止过度归约与归约不足。我们在合成数据集上验证了理论发现，并在七个基准测试上的实验表明，MGEC 优于现有最先进方法。