Group Resonance Network: Learnable Prototypes and Multi-Subject Resonance for EEG Emotion Recognition

TL;DR

This paper introduces the Group Resonance Network (GRN), a novel EEG emotion recognition model that combines individual features with group resonance modeling to improve cross-subject accuracy.

Contribution

The paper proposes a new model integrating learnable prototypes and multi-subject resonance to better exploit shared group regularities in EEG data.

Findings

GRN outperforms baselines on SEED and DEAP datasets.

Resonance modeling improves cross-subject recognition.

Prototype learning provides complementary benefits.

Abstract

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.