The Saturation Regularity: Rethinking Concept-Aligned Supervision in EEG Learning

Concept-aligned supervision aims to improve learning by making task-relevant knowledge explicit in a model's representations. However, a concept's relevance does not establish the value of further supervising it once the model already encodes it. Choosing auxiliary supervision therefore requires understanding when stronger alignment improves prediction and what information remains useful when it does not. In EEG emotion recognition, we identify the Saturation Regularity: further concept supervision can strengthen alignment without detectable predictive gains, and can even reduce accuracy. This pattern varies across FACED training configurations and CIFAR-10 training stages. Random-target controls further show that auxiliary-loss gains need not depend on semantic content. To examine what remains useful beyond class-mean alignment, we introduce ResDiag, a residual diagnostic framework combining representation decomposition, directional ablation, and ensemble-contribution analysis. ResDiag reveals that within-class concept-related residuals retain predictive information and that their encoding strength is associated with ensemble contribution. Motivated by these supervision and residual analyses, we develop SatEnsemble, a saturation-informed ensemble learning framework. It strengthens individual models with a refined training recipe, then averages checkpoint predictions to exploit complementary information. Experiments show that SatEnsemble outperforms leading EEG methods on FACED, setting a new state of the art and establishing an effective strategy for learning beyond concept-aligned supervision.