论文标题
基于脑电图的大脑监测的调查和教程，以进行驾驶员状态分析
A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis
论文作者
论文摘要
驾驶员的认知和生理状态会影响他们控制车辆的能力。因此，这些驾驶员状态对汽车的安全很重要。高级驾驶员援助系统（ADA）或自动驾驶汽车的设计将取决于它们与驾驶员有效互动的能力。因此，对驾驶员状态的更深入了解至关重要。事实证明，脑电图是驾驶员状态监测和人为错误检测的最有效方法之一。本文讨论了过去三十年来基于EEG的驾驶员状态检测系统及其相应的分析算法。首先，引入了用于驾驶员状态研究的常用EEG系统设置。然后，审查了脑电图预处理，特征提取和分类算法的驱动程序状态检测。最后，深入审查了基于EEG的驾驶员状态监测研究，并讨论了其未来的发展。结论是，当前基于EEG的驾驶员状态监测算法有望用于安全应用。但是，在减少脑电图伪像，实时处理和受试者之间的分类准确性中，仍然需要进行许多改进。
Drivers cognitive and physiological states affect their ability to control their vehicles. Thus, these driver states are important to the safety of automobiles. The design of advanced driver assistance systems (ADAS) or autonomous vehicles will depend on their ability to interact effectively with the driver. A deeper understanding of the driver state is, therefore, paramount. EEG is proven to be one of the most effective methods for driver state monitoring and human error detection. This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades. First, the commonly used EEG system setup for driver state studies is introduced. Then, the EEG signal preprocessing, feature extraction, and classification algorithms for driver state detection are reviewed. Finally, EEG-based driver state monitoring research is reviewed in-depth, and its future development is discussed. It is concluded that the current EEG-based driver state monitoring algorithms are promising for safety applications. However, many improvements are still required in EEG artifact reduction, real-time processing, and between-subject classification accuracy.
