动态捕捉脑区关系，提升情绪识别准确率
Adaptive Progressive Attention Graph Neural Network for EEG Emotion Recognition
- 三阶段专家网络逐层分析脑区拓扑结构
- 在三个数据集上优于现有方法，提升识别精度
- 适合脑电情绪分析与神经科学交叉研究者
近年来大量神经科学研究表明，特定脑区与人类情绪反应相关，且这些区域在个体间和情绪状态中存在差异。为充分挖掘此类神经模式，我们提出自适应渐进注意力图神经网络（APAGNN），动态捕捉情绪处理过程中脑区间的空间关系。APAGNN包含三个专用专家，分层分析脑拓扑：第一阶段捕获全局脑模式，第二阶段关注区域特异性特征，第三阶段分析情绪相关通道。该分层方法实现对神经活动的逐步精细化分析。此外，权重生成器融合三者输出，平衡贡献以生成最终预测标签。在SEED、SEED-IV和MPED数据集上的大量实验表明，该方法显著提升脑电情绪识别性能，优于基线模型。
原文摘要 · Abstract (English)
In recent years, numerous neuroscientific studies demonstrate that specific areas of the brain are connected to human emotional responses, with these regions exhibiting variability across individuals and emotional states. To fully leverage these neural patterns, we propose an Adaptive Progressive Attention Graph Neural Network (APAGNN), which dynamically captures the spatial relationships among brain regions during emotional processing. The APAGNN employs three specialized experts that progressively analyze brain topology. The first expert captures global brain patterns, the second focuses on region-specific features, and the third examines emotion-related channels. This hierarchical approach enables increasingly refined analysis of neural activity. Additionally, a weight generator integrates the outputs of all three experts, balancing their contributions to produce the final predictive label. Extensive experiments conducted on SEED, SEED-IV and MPED datasets indicate that our method enhances EEG emotion recognition performance, achieving superior results compared to baseline methods.
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。