ERP-XTTN: Interpretable Prototype-Guided Cross-Attention for Cross-Subject ERP Classification
Researchers have developed ERP-XTTN, a novel cross-attention architecture designed for interpretable brain-computer interface classification. This model routes input EEG patches to fixed difference-wave prototypes, enabling cross-subject generalization without calibration. Evaluations across multiple public datasets and ERP components show ERP-XTTN achieves competitive accuracy while offering transparent signal structure insights, unlike black-box models. AI
IMPACT Introduces a new method for interpretable BCI classification, potentially improving user trust and diagnostic accuracy in neurological applications.