TRACE: Learning to Adapt Spatial–Spectral Relations for Cross-Dataset EEG Emotion Recognition

Cross-dataset EEG emotion recognition is challenging because spatial–spectral relations that are useful for one subject or recording condition may not transfer to another. We show that even within predictive spatial relation groups, frequency- specific relations can have different effects, and useful adjustments vary across targets. We therefore propose TRACE (Target-conditioned Relation Adaptation for Cross-dataset EEG), which learns rules for adjusting spatial–spectral rela- tions from labeled source domains and applies these rules to each unlabeled tar- get. TRACE represents channel–band interactions with shared relation types and adapts target-specific adjustments to their relative weights from a domain repre- sentation aggregated from unlabeled target trials. It learns these rules from source- domain adjustments validated by lower classification loss on separate source trials and constrains target coefficients to a source-supported region defined by source- domain predictions. TRACE projects the difference between graph representa- tions before and after relation adjustments into a residual that refines the base EEG representation learned directly from the input features for emotion classification. Across eight transfer directions among four public EEG emotion datasets, TRACE achieves the highest accuracy among the compared methods in every direction. Target-substitution experiments further show that adjustments inferred for the cur- rent target outperform those transferred from other targets, while TRACE remains robust to electrode masking and feature noise. These results support learning how relations adapt rather than enforcing a fixed structure across domains.