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

arXiv:2603.11119v1 Announce Type: new 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 ba

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Renwei Meng · · 1 min read · 12 views
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arXiv:2603.11119v1 Announce Type: new 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.

Executive Summary

This article proposes the Group Resonance Network (GRN), a novel approach to EEG emotion recognition in cross-subject settings. GRN integrates individual EEG dynamics with offline group resonance modeling to capture stimulus-locked group regularities shared across subjects. The network consists of three components: an individual encoder, a set of learnable group prototypes, and a multi-subject resonance branch. Experiments on SEED and DEAP datasets demonstrate that GRN outperforms competitive baselines and ablation studies confirm the complementary benefits of prototype learning and multi-subject resonance modeling. The proposed method has the potential to improve EEG emotion recognition accuracy and shed light on the neural mechanisms underlying emotional processing.

Key Points

  • ▸ GRN integrates individual EEG dynamics with offline group resonance modeling to capture stimulus-locked group regularities
  • ▸ The network consists of three components: individual encoder, learnable group prototypes, and multi-subject resonance branch
  • ▸ Experiments demonstrate that GRN outperforms competitive baselines and ablation studies confirm the complementary benefits of prototype learning and multi-subject resonance modeling

Merits

Strength in Addressing Inter-Subject Variability

GRN effectively addresses the challenge of inter-subject variability in EEG emotion recognition by incorporating group resonance modeling, which captures stimulus-locked group regularities shared across subjects.

Improved Accuracy and Efficiency

The proposed method demonstrates improved accuracy and efficiency compared to competitive baselines, making it a promising approach for real-world applications.

Demerits

Limited Generalizability to Other EEG Applications

The method's effectiveness is specifically tailored to EEG emotion recognition, and its generalizability to other EEG applications, such as motor control or cognitive processing, remains to be explored.

Computational Requirements

The multi-subject resonance branch and the learnable group prototypes may increase the computational requirements for the GRN, which could be a limitation for some real-world applications.

Expert Commentary

The proposed GRN method is a significant contribution to the field of EEG emotion recognition, as it effectively addresses the challenge of inter-subject variability. The incorporation of group resonance modeling is a novel aspect of the method, and its benefits are clearly demonstrated through experiments and ablation studies. However, the method's limited generalizability to other EEG applications and increased computational requirements are potential limitations. Nevertheless, the GRN method has the potential to improve EEG emotion recognition accuracy and shed light on the neural mechanisms underlying emotional processing.

Recommendations

  • ✓ Future studies should explore the generalizability of the GRN method to other EEG applications and investigate its performance in real-world scenarios.
  • ✓ The development of more efficient and scalable implementations of the GRN method would be beneficial for its practical applications.

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