UGPA-AAD: Uncertainty-aware Gaussian Graph and Prototype Adaptation for Cross-Subject Auditory Attention Decoding

Auditory Attention Decoding (AAD) plays a vital role in enabling intelligent auditory brain-computer interfaces by decoding attention-related neural responses from EEG signals. Currently, cross-subject AAD has gained increasing attention, aiming to learn subject-invariant attention-related representations that generalize to target subjects. Despite recent advancements, cross-subject AAD still faces two core challenges under subject-specific variations and signal disturbances: unreliable spatial dependency modeling and cross-subject semantic inconsistency. To address these challenges, we propose an Uncertainty-aware Gaussian Graph and Prototype Adaptation framework (UGPA-AAD), which improves representation reliability and cross-subject semantic consistency through uncertainty-aware structural learning and prototype-guided semantic alignment. Specifically, we first introduce a Multi-Scale Feature Encoder (MSFE) to capture informative neural representations by integrating complementary representations across multiple temporal receptive fields. Then, to mitigate unreliable channel interactions under subject variability and signal disturbances, we design an Uncertainty-Aware Gaussian Graph Encoder (UGGE) that leverages channel-wise uncertainty to modulate spatial dependencies and construct robust structural representations. Finally, Prototype-Guided Cross-Subject Learning (PCSL) is designed to alleviate class-wise semantic inconsistency across subjects by constructing global class prototypes as semantic anchors to align representations across subjects toward consistent class structures, thereby enhancing semantic discriminability. Experimental results on four benchmarks demonstrate that our method consistently outperforms existing approaches in both audio-only and audio-visual scenarios. Our code will be released upon acceptance.