ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification

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 event-related potential (ERP) classification across diverse paradigms under deployment-compatible conditions.

We propose ERP-XTTN (ERP Cross-Attention), a cross-attention architecture that routes input electroencephalographic 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 the 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 leave-one-subject-out (LOSO) cross-validation with causal filtering at a three-channel montage, compared against EEGNet, EEG-Deformer, ERP Prototypical Matching Net (EPMN), and xDAWN with Riemannian geometry (xDAWN+RG).

At three channels, the mean performance gap between the best baseline and ERP-XTTN was 0.025 area under the receiver operating characteristic curve (AUROC). Prototype interventions confirmed that decisions depend on the physiological content of the prototypes rather than on the routing attention pattern alone. False positives morphologically resembled true positives more than true negatives did across all datasets, indicating 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 at a three-channel montage. Unlike post-hoc explanation methods for black-box models, the basis of each decision is directly observable in the trained model itself. To our knowledge, this is the first epoch-level LOSO benchmark on ERP CORE.