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

Renwei Meng

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.

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

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.
Paper Structure (22 sections, 6 equations, 4 figures, 4 tables)

This paper contains 22 sections, 6 equations, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. EEG Emotion Recognition and Cross-Subject Learning
  4. Inter-Subject Synchrony and Offline Resonance Modeling
  5. Prototype Learning and Temporal Modeling
  6. Methodology
  7. Overview
  8. Input Representation and Individual Encoding
  9. Learnable Group Prototypes for Prototype-Induced Resonance
  10. Multi-Subject Resonance Tensor
  11. Resonance-Aware Fusion and Classification
  12. Training Objective
  13. Experiments
  14. Datasets
  15. Protocols
  16. ...and 7 more sections

Figures (4)

  • Figure 1: Overall GRN pipeline.
  • Figure 2: Multi-subject resonance tensor construction
  • Figure 3: Confusion matrices on SEED: (a) SI (LOSO) and (b) SD.
  • Figure 4: Training/validation curves on SEED SI: loss and accuracy vs. epoch.