Group Resonance Network: Learnable Prototypes and Multi-Subject Resonance for EEG Emotion Recognition
This addresses cross-subject variability in EEG emotion recognition, an incremental improvement for affective computing applications.
The paper tackled the challenge of cross-subject EEG emotion recognition by proposing the Group Resonance Network (GRN) to exploit group regularities, and it outperformed baselines on SEED and DEAP datasets in subject-dependent and leave-one-subject-out protocols.
Electroencephalography(EEG)-basedemotionrecognitionre- mains challenging in cross-subject settings due to severe inter-subject variability. Existing methods mainly learn subject-invariant features, but often under-exploit stimulus-locked group regularities shared across sub- jects. To address this issue, we propose the Group Resonance Network (GRN), which integrates individual EEG dynamics with offline group resonance modeling. GRN contains three components: an individual en- coder for band-wise EEG features, a set of learnable group prototypes for prototype-induced resonance, and a multi-subject resonance branch that encodes PLV/coherence-based synchrony with a small reference set. A resonance-aware fusion module combines individual and group-level rep- resentations for final classification. Experiments on SEED and DEAP under both subject-dependent and leave-one-subject-out protocols show that GRN consistently outperforms competitive baselines, while abla- tion studies confirm the complementary benefits of prototype learning and multi-subject resonance modeling.