融合局部与全局特征，提升跨被试脑电情绪识别准确率
Local-Global Feature Fusion for Subject-Independent EEG Emotion Recognition
- 分通道提取局部特征，整合时域谱图复杂度信息
- 在SEED-VII数据集上实现约40%的7分类平均准确率
- 适合需要跨被试泛化的脑电情绪分析研究者
跨被试脑电情绪识别面临显著个体差异及短时、噪声干扰数据难以学习鲁棒表示的挑战。为此，我们提出一种融合框架，结合（i）通道级局部描述符和（ii）试次级全局描述符，提升了在SEED-VII数据集上的跨被试泛化能力。局部表示通过拼接微分熵与图论特征构建，全局表示则汇总试次层面的时间域、频域及复杂度特征。两者在双分支变换器中通过注意力机制融合，并引入领域对抗正则化，样本经强度阈值过滤。在留一被试剔除协议下实验表明，该方法持续优于单视图与经典基线，在7类情绪识别中达到约40%的平均准确率。代码已公开于https://github.com/Danielz-z/LGF-EEG-Emotion。
原文摘要 · Abstract (English)
Subject-independent EEG emotion recognition is challenged by pronounced inter-subject variability and the difficulty of learning robust representations from short, noisy recordings. To address this, we propose a fusion framework that integrates (i) local, channel-wise descriptors and (ii) global, trial-level descriptors, improving cross-subject generalization on the SEED-VII dataset. Local representations are formed per channel by concatenating differential entropy with graph-theoretic features, while global representations summarize time-domain, spectral, and complexity characteristics at the trial level. These representations are fused in a dual-branch transformer with attention-based fusion and domain-adversarial regularization, with samples filtered by an intensity threshold. Experiments under a leave-one-subject-out protocol demonstrate that the proposed method consistently outperforms single-view and classical baselines, achieving approximately 40% mean accuracy in 7-class subject-independent emotion recognition. The code has been released at https://github.com/Danielz-z/LGF-EEG-Emotion.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。