SCoRE: Learnable Reflection Equivariance for Cross-Subject EEG Decoding

Electroencephalography (EEG) is the principal non-invasive modality for brain-computer interfaces (BCI). Generalizing across pronounced inter-individual variability has long been a crucial challenge for cross-subject decoding. An intuitive strategy for addressing this challenge is to scale up: pretrain high-capacity models on large corpora to obtain subject-invariant representations. Yet scaling up increases model complexity and inference cost, and the shared structure such models capture remains implicit in their weights. We instead propose SCoRE, a method that remains compact while making this structure explicit through reflection equivariance, an inductive bias for EEG decoding. Physiologically, its most familiar manifestation is contralateral motor control: each hemisphere controls the opposite hand, and hand movements evoke mirror-image activity across the scalp. We posit that reflection equivariance is not confined to motor control but is intrinsic to brain activity. We operationalize this property by constraining the classifier such that a spatial reflection of the signal induces a corresponding transformation of class evidence. Specifically, SCoRE trains a shared network on each input and its reflection, constructing an equivariant classifier through joint training and parameterization. This classifier separately captures class evidence that reverses under reflection and that remains invariant. Notably, SCoRE consistently outperforms compact EEG baselines by 10.37 accuracy points and surpasses state-of-the-art pretrained models at a fraction of their size. Our method not only advances cross-subject decoding but also offers neuroscientific insight. The learned input reflection and output transformation are interpretable and agree with established physiology. SCoRE, together with EEG experiments, opens a new pathway for discovering reflection-equivariant structure across tasks and brain states.