用生理原型引导注意力，实现无需校准的可解释脑机接口分类

Interpretable brain-computer interface classifiers that generalize across subjects without calibration remain an open challenge. We evaluated whether prototype-based cross-attention can provide competitive, inherently interpretable ERP classification across paradigms under deployment-compatible conditions. We propose ERP-XTTN (ERP Cross-Attention), a cross-attention architecture that routes input EEG peaks to fixed difference-wave prototypes via query-key-only cross-attention with no value projection. Classification is based directly on prototype similarity and a separate measure of component amplitude, so that prototype content contributes to every decision by construction. Prototypes are derived automatically from prominent extrema in the training-fold grand-average difference wave. We evaluated across three public sources (BNCI Horizon 2020, HRI Cursor, and ERP CORE) encompassing eight ERP components (ERN, LRP, ErrP, N170, P300, N2pc, MMN, N400). Evaluations used LOSO cross-validation with causal filtering at a three-channel montage, compared against EEGNet, EEG-Deformer, EPMN, and xDAWN with Riemannian geometry. The mean performance gap between the best baseline and ERP-XTTN was 0.025 AUROC. Prototype interventions confirmed that decisions depend on prototype content rather than on the routing attention pattern alone. False positives morphologically resembled true positives more than true negatives did, so classification errors are neurophysiologically explicable. ERP-XTTN generalizes across diverse ERP morphologies under causal, calibration-free conditions, while retaining competitive performance and decisions that depend directly on physiological prototype content. Unlike post-hoc explanation methods for black-box models, the basis of each decision is directly observable in the trained model. To our knowledge, this is the first epoch-level LOSO benchmark on ERP CORE.