Group Resonance Network: Learnable Prototypes and Multi-Subject Resonance for EEG Emotion Recognition
Electroencephalography (EEG)-based emotion recognition remains 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 subjects. 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 encoder 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 reson...
| Authors | Renwei Meng |
|---|---|
| Primary category | cs.LG |
| Published | 11 March 2026 |
| Last updated at source | 30 August 2026 |
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