Driver drowsiness electroencephalography (EEG) signal monitoring can timely alert drivers of their drowsiness status, thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end lightweight dual graph convolutional network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. Furthermore, to deploy our method on the fourth-generation Raspberry Pi, we utilize Adaptive Pruning Optimization (APO) on both channels and neurons, reducing inference latency by almost half. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization compared to existing state-of-the-art algorithms. All our source code can be found at https://github.com/BryantDom/Driver-Drowsiness-Monitoring.

翻译：驾驶员疲劳脑电图信号监测能够及时预警驾驶员的疲劳状态，从而降低交通事故发生概率。图卷积网络在处理脑电图信号的非平稳、时变及非欧几里得特性方面已展现出显著优势。然而，现有单通道脑电图邻接图构建过程缺乏可解释性，这限制了图卷积网络有效提取邻接图特征的能力，进而影响疲劳监测性能。为解决该问题，本文提出一种边缘端轻量级双图卷积网络。具体而言，我们首次引入神经生理学知识构建基准疲劳状态邻接图，用以表征驾驶员疲劳状态。此外，为在有限脑电图数据中表达更丰富的特征，我们提出增强图级模块。该模块在图层级捕获全局与局部信息，在保持基准疲劳状态邻接图特征完整性的同时增强有效特征表达能力。进一步地，为将本方法部署于第四代树莓派设备，我们在通道与神经元层面采用自适应剪枝优化策略，使推理延迟降低近半。在基准数据集上的实验表明，相较于现有先进算法，本网络在监测性能与硬件资源利用率之间实现了最佳平衡。全部源代码已公开于https://github.com/BryantDom/Driver-Drowsiness-Monitoring。