Learning EEG Representations via Topology-Aware Privileged Distillation

Audio and visual signals offer rich affective cues for emotion recognition, but they are identity-sensitive and raise privacy concerns at deployment. EEG is an appealing inference modality because it does not directly expose facial appearance or voice content, yet its low signal-to-noise ratio often limits recognition performance. We therefore adopt a cross-subject privileged learning setup, where audio and video are used only during training, enabling an EEG-only model at inference. However, existing cross-modal distillation objectives often overlook the region-level interaction structure inherent in EEG, even though affective processing is reflected in inter-regional dependencies. To address this, we propose Topology-Aware Relational Distillation (TRD): the teacher induces a region-dependency graph from audio-visually refined EEG region features, and the student aligns its induced graph by matching the global edge distribution rather than directly imitating features. Meanwhile, the modality gap remains a barrier because EEG representations can still be semantically misaligned with the teacher’s multimodal decision space. To mitigate this gap, we further introduce Masked Decision-Space Alignment (MDA). MDA injects student EEG region features into a frozen teacher decision pathway with privileged modalities masked, and distills the resulting predictions so that the student can inherit multimodal semantics without requiring privileged modalities at inference. Under cross-subject EEG-only inference on EAV and MDMER, our method consistently improves the same student backbone trained without distillation, yielding up to +2.09 pp accuracy and +4.46 pp Macro-F1.