通过可学习原型与群体共振，提升跨被试脑电情绪识别性能
Group Resonance Network: Learnable Prototypes and Multi-Subject Resonance for EEG Emotion Recognition
- 引入可学习的群体原型与多被试同步性建模，融合个体与群体特征
- 在SEED和DEAP数据集上准确率超越现有方法，跨被试测试提升显著
- 适合研究脑电情绪识别、个性化模型泛化与群体神经动力学的学者
基于脑电（EEG）的情绪识别在跨被试场景中仍具挑战性，主要源于个体间差异显著。现有方法多聚焦于学习跨被试不变特征，却常忽视被试间共享的刺激锁定群体规律。为此，本文提出群体共振网络（GRN），融合个体脑电动态与离线群体共振建模。GRN包含三部分：用于频带特征提取的个体编码器、一组可学习的群体原型以诱导共振，以及基于相位锁定值（PLV）/相干性的多被试同步性建模分支，使用小规模参考集进行编码。一个共振感知融合模块整合个体与群体表示以完成分类。在SEED与DEAP数据集上，无论是被试内还是留一被试外设置，GRN均持续优于竞争基线；额外分析验证了原型学习、PLV/相干性共振及安全参考构建的有效性。
原文摘要 · Abstract (English)
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 resonance-aware fusion module combines individual and group-level representations 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 additional analyses confirm the effects of prototype learning, PLV/coherence resonance, and leakage-safe reference construction.
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