提出双注意力混合网络，提升脑电情绪识别的准确率与可解释性。
DAH-Net: A Dual-Attention Hybrid Network for Interpretable and Robust EEG-Based Emotion Recognition
- 融合1D-CNN、BiLSTM与双注意力机制，增强特征提取能力。
- 在2479样本上达99.19%准确率，优于多种主流模型。
- 揭示协方差特征关键作用，适合需可解释性的脑机接口应用。
基于脑电的情绪识别支持情感化脑机接口与心理健康监测，但面临信号复杂、个体差异大和可解释性差等挑战。本文提出DAH-Net，一种结合1D-CNN、BiLSTM与双多头注意力（16+8头）的混合网络，用于三类脑电情绪分类。在包含2,479个样本、988个脑电特征的数据集上，DAH-Net实现99.19%的留出测试准确率，训练-测试差距仅0.81%，显著优于随机森林（96.17%）、SVM（96.77%）、MLP（97.18%）和Transformer（98.19%）基线。弗里德曼检验（χ² = 28.54，p < 0.001）与事后威尔科克斯森比较确认结果统计显著。通过随机森林重要性、SHAP归因与特征类别隔离分析发现，协方差特征单独使用即达94.96%基准准确率，而特征值特征虽单独表现较差（84.07%），却提供紧凑互补信息。模型参数量仅3.33M（约13.3MB，32位权重），具备未来轻量化情感计算潜力，但需进一步开展无监督及外部验证。
原文摘要 · Abstract (English)
EEG-based emotion recognition supports affective brain-computer interfaces and mental health monitoring yet remains challenged by signal complexity, subject variability, and limited interpretability. We propose DAH-Net, a dual-attention hybrid network integrating 1D-CNN, BiLSTM, and dual multi-head attention (16+8 heads) for three-class EEG emotion classification. Evaluated on 2,479 samples with 988 EEG features, DAH-Net achieves 99.19% held-out test accuracy with a 0.81% train-test gap, outperforming RF (96.17%), SVM (96.77%), MLP (97.18%), and Transformer (98.19%) baselines. Friedman testing (\c{hi}2 = 28.54, p < 0.001) and post-hoc Wilcoxon comparisons confirm statistical significance. Feature-level analysis using Random Forest importance, SHAP attribution, and feature category isolation shows that covariance features achieve near-baseline standalone accuracy (94.96%), while eigenvalue features show limited standalone performance (84.07%) but provide compact complementary information. The compact architecture (3.33M parameters, approximately 13.3MB using 32-bit weights) suggests potential for future lightweight EEG-based affective computing, pending subject-independent and external validation.
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