NeuroContext-TTA: Source-Anchored Context Calibration for Cross-Subject EEG Emotion Recognition

Cross-subject EEG emotion recognition must accommodate changes in signal statistics without labels from the new subject. Test-time adaptation can exploit recent observations, but a short, correlated history may not represent every emotion class. Building target class memories from its predictions can therefore entangle subject variation with incomplete class coverage. We propose NeuroContext-TTA, a forward-only adaptation framework that separates source class structure from target context. It translates all source prototypes by a shared context-derived offset, retaining a reference for every class without assigning target pseudo-labels. Retrieval combines similarities in the EEG embedding and encoded log-covariance spaces, and a query-level reliability gate controls residual feature and logit corrections. The calibration modules are trained on source subjects and remain frozen at deployment; only the context statistics and translated references change. Across SEED, SEED-IV, FACED-3 and FACED-9, calibration of mdJPT features improves macro-F1 by 1.35, 0.54, 5.09 and 0.52 percentage points, respectively. Evaluation with five EEG backbones further yields accuracy gains in 19 of 20 backbone–task pairs. These results support source-anchored calibration as a practical way to use unlabeled context without online parameter updates.