Unraveling Brain Signals through Expert Collaboration: Mechanism-Guided Routing for Heterogeneous EEG Learning

EEG decoding across heterogeneous paradigms requires models to capture distinct yet complementary signal characteristics. Motor imagery, error-related potentials, and steady-state visual evoked potentials place different demands on temporal, spectral, and spatial representations, making it challenging for a single representation to capture the cues relevant to each paradigm. We propose MGNet, a mechanism-guided expert-routing framework that enables adaptive collaboration among specialized experts while preserving their complementary representations. MGNet combines a gated causal temporal model with three experts: a temporal module, a spectral dynamic graph module, and a Riemannian covariance module. A lightweight router and a Manhattan-distance-based fusion mechanism adaptively integrate expert representations to accommodate paradigm-dependent EEG characteristics. Experiments on three public benchmarks—BCI Competition IV-2a, BCI Challenge, and MAMEM—show that MGNet consistently outperforms the evaluated state-of-the-art baselines. Ablation studies further reveal paradigm-dependent expert contributions consistent with the signal characteristics motivating their design. These results support mechanism-guided specialization and adaptive expert collaboration as an effective approach to multi-paradigm EEG decoding.