Driver drowsiness is one of main factors leading to road fatalities and hazards in the transportation industry. Electroencephalography (EEG) has been considered as one of the best physiological signals to detect drivers drowsy states, since it directly measures neurophysiological activities in the brain. However, designing a calibration-free system for driver drowsiness detection with EEG is still a challenging task, as EEG suffers from serious mental and physical drifts across different subjects. In this paper, we propose a compact and interpretable Convolutional Neural Network (CNN) to discover shared EEG features across different subjects for driver drowsiness detection. We incorporate the Global Average Pooling (GAP) layer in the model structure, allowing the Class Activation Map (CAM) method to be used for localizing regions of the input signal that contribute most for classification. Results show that the proposed model can achieve an average accuracy of 73.22% on 11 subjects for 2-class cross-subject EEG signal classification, which is higher than conventional machine learning methods and other state-of-art deep learning methods. It is revealed by the visualization technique that the model has learned biologically explainable features, e.g., Alpha spindles and Theta burst, as evidence for the drowsy state. It is also interesting to see that the model uses artifacts that usually dominate the wakeful EEG, e.g., muscle artifacts and sensor drifts, to recognize the alert state. The proposed model illustrates a potential direction to use CNN models as a powerful tool to discover shared features related to different mental states across different subjects from EEG signals.

翻译：电脑扫描(EEG)被认为是检测司机潜伏状态的最佳生理信号之一,因为它直接测量大脑中的神经生理活动。然而,设计一个无校准的系统用于与EEEG一起检测司机潜伏状态仍是一项艰巨的任务,因为EEG在不同主题之间有着严重的精神和物理漂移。在本文中,我们提议建立一个紧凑和可解释的 Convolution Neal网络(CNN),以发现不同科目的EEEG特征,用于检测驱动潜伏状态。我们在模型结构中引入了全球平均智能集合(GAP)层,允许在大脑中直接测量神经生理活动。然而,设计一个无校准的系统,用于与EEGEG一起检测,因为EGS在不同主题中存在严重的精神和物理漂移状态。结果显示,拟议的模型可以达到11个科目的平均准确度为73.22%,这比常规的机器学习方法和其他高级的深层次学习方法要高。我们用全球平均智能集合(GAGA) 将不同的智能智能定位显示,而模型也用来解释了EGEGRRLS, 的深度模型和BRODLS,用来解释。