OrthoTSS：通过正交引导的任务-受试者分离实现跨受试者 P300 解码的零校准框架

OBJECTIVE: Cross-subject P300 decoding remains challenging for zero-calibration brain-computer interfaces (BCIs), as inter-subject variability must be reduced while preserving weak task-relevant neural information.

METHODS: We propose Orthogonality-guided Task-Subject Separation (OrthoTSS), integrating a multi-scale spatio-temporal frontend, dual bidirectional Mamba streams, and orthogonality regularization. The task stream performs target/non-target decoding, while an auxiliary domain stream models subject-related variability during training. The regularization reduces linear cross-stream coupling and promotes functional differentiation without assuming complete disentanglement.

RESULTS: Under leave-one-subject-out (LOSO) evaluation, OrthoTSS achieved 76.35% balanced accuracy on PhysioNet ERP and 86.75% on Naturalistic Search FRP. It showed favorable performance against representative architectural, recent cross-subject, and domain-generalization baselines. Ablation and representation analyses further indicated reduced cross-stream similarity and relative branch specialization while preserving task-discriminative structure.

CONCLUSION: OrthoTSS improves cross-subject P300 decoding by promoting functional differentiation between task-related and subject-related representations while retaining discriminative neural information.

SIGNIFICANCE: This task-preservation-oriented framework provides offline evidence toward practical zero-calibration P300 BCI decoding.