Electroencephalography (EEG) signals have been proven to be one of the most predictive and reliable indicators for estimating driving fatigue state. However, how to make full use of EEG data for driving fatigue detection remains a challenge. Many existing methods include a time-consuming manual process or tedious parameter tunings for feature extraction, which is inconvenient to train and implement. On the other hand, most models ignore or manually determine EEG connectivity features between different channels, thus failing to thoroughly exploit the intrinsic interchannel relations for classification. In this article, we introduce a new attention-based multiscale convolutional neural network-dynamical graph convolutional network (AMCNN-DGCN) model, aiming to conquer these two issues in a unified end-to-end model. AMCNN-DGCN starts with attention-based multiscale temporal convolutions to automatically learn frequency filters to extract the salient pattern from raw EEG data. Subsequently, AMCNN-DGCN uses dynamical graph convolutional networks (DGCNs) to learn spatial filters, in which the adjacency matrix is adaptively determined in a data-driven way to exploit the intrinsic relationship between channels effectively. With the temporal–spatial structure, AMCNN-DGCN can capture highly discriminative features. To verify the effectiveness of AMCNN-DGCN, we conduct a simulated fatigue driving environment to collect EEG signals from 29 healthy subjects (male/female = 17/12 and age = 23.28±2.70 years) through a remote wireless cap with 24 channels. The results demonstrate that our proposed model outperforms six widely used competitive EEG models with high accuracy of 95.65%. Finally, the critical brain regions and connections for driving fatigue detection were investigated through the dynamically learned adjacency matrix.

中文翻译：

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将基于注意力的多尺度 CNN 与动态 GCN 联系起来进行驾驶疲劳检测

脑电图 (EEG) 信号已被证明是估计驾驶疲劳状态的最具预测性和最可靠的指标之一。然而，如何充分利用脑电数据进行驾驶疲劳检测仍然是一个挑战。许多现有方法包括耗时的手动过程或繁琐的特征提取参数调整，不便于训练和实施。另一方面，大多数模型忽略或手动确定不同通道之间的 EEG 连接特征，从而未能彻底利用内在的通道间关系进行分类。在本文中，我们介绍了一种新的基于注意力的多尺度卷积神经网络-动态图卷积网络 (AMCNN-DGCN) 模型，旨在在统一的端到端模型中克服这两个问题。AMCNN-DGCN 从基于注意力的多尺度时间卷积开始，自动学习频率滤波器，从原始 EEG 数据中提取显着模式。随后，AMCNN-DGCN 使用动态图卷积网络 (DGCN) 来学习空间滤波器，其中以数据驱动的方式自适应地确定邻接矩阵，以有效利用通道之间的内在关系。凭借时空结构，AMCNN-DGCN 可以捕获高度判别性的特征。为了验证 AMCNN-DGCN 的有效性，我们进行了模拟疲劳驾驶环境，通过具有 24 个通道的远程无线帽收集 29 名健康受试者（男性/女性 = 17/12 和年龄 = 23.28±2.70 岁）的 EEG 信号。结果表明，我们提出的模型优于六种广泛使用的竞争性 EEG 模型，准确率高达 95.65%。最后，通过动态学习的邻接矩阵研究了驾驶疲劳检测的关键大脑区域和连接。