用图卷积和精准注意力提升脑电疲劳检测准确率
Exact Fit Attention in Node-Holistic Graph Convolutional Network for Improved EEG-Based Driver Fatigue Detection
- 构建节点整体图卷积网络，动态学习脑电通道特征
- 跨被试准确率提升15.06%，内被试最高提升3.42%
- 揭示顶枕区与前颞区在疲劳和警觉中的关键作用
基于脑电的疲劳监测可有效降低交通事故发生率。过去十年，随着深度学习发展，卷积神经网络（CNN）被广泛用于脑电信号处理。然而，由于脑电数据具有非欧几里得特性，现有CNN可能丢失重要空间信息，尤其是通道间的相关性。为此，本文提出节点整体图卷积网络（NHGNet），利用图卷积动态学习各通道特征，并通过可训练邻接矩阵实现精准注意力优化，捕捉通道间关联。模型增强了可解释性，揭示了不同心理状态下大脑活动的关键区域及其相互关系。在两个公开数据集上的验证表明，NHGNet优于当前最优方法：在同被试设置下，准确率提升至少2.34%和3.42%；在跨被试设置下，提升至少2.09%和15.06%。可视化分析显示，中央顶区对疲劳水平检测至关重要，而额叶和颞叶则对维持警觉性起关键作用。
原文摘要 · Abstract (English)
EEG-based fatigue monitoring can effectively reduce the incidence of related traffic accidents. In the past decade, with the advancement of deep learning, convolutional neural networks (CNN) have been increasingly used for EEG signal processing. However, due to the data's non-Euclidean characteristics, existing CNNs may lose important spatial information from EEG, specifically channel correlation. Thus, we propose the node-holistic graph convolutional network (NHGNet), a model that uses graphic convolution to dynamically learn each channel's features. With exact fit attention optimization, the network captures inter-channel correlations through a trainable adjacency matrix. The interpretability is enhanced by revealing critical areas of brain activity and their interrelations in various mental states. In validations on two public datasets, NHGNet outperforms the SOTAs. Specifically, in the intra-subject, NHGNet improved detection accuracy by at least 2.34% and 3.42%, and in the inter-subjects, it improved by at least 2.09% and 15.06%. Visualization research on the model revealed that the central parietal area plays an important role in detecting fatigue levels, whereas the frontal and temporal lobes are essential for maintaining vigilance.
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