Peng Yong, Xu Qian, Lin Shuxiang, Wang Xinghua, Xiang Guoliang, Huang Shufang, Zhang Honghao, Fan Chaojie
Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha, China.
School of Business and Trade, Hunan Industry Polytechnic, Changsha, China.
Front Psychol. 2022 Jul 22;13:919695. doi: 10.3389/fpsyg.2022.919695. eCollection 2022.
The driver is one of the most important factors in the safety of the transportation system. The driver's perceptual characteristics are closely related to driving behavior, while electroencephalogram (EEG) as the gold standard for evaluating human perception is non-deceptive. It is essential to study driving characteristics by analyzing the driver's brain activity pattern, effectively acquiring driver perceptual characteristics, creating a direct connection between the driver's brain and external devices, and realizing information interchange. This paper first introduces the theories related to EEG, then reviews the applications of EEG in scenarios such as fatigue driving, distracted driving, and emotional driving. The limitations of existing research have been identified and the prospect of EEG application in future brain-computer interface automotive assisted driving systems have been proposed. This review provides guidance for researchers to use EEG to improve driving safety. It also offers valuable suggestions for future research.
驾驶员是交通系统安全的最重要因素之一。驾驶员的感知特性与驾驶行为密切相关，而脑电图（EEG）作为评估人类感知的金标准是无欺骗性的。通过分析驾驶员的大脑活动模式来研究驾驶特性、有效获取驾驶员感知特性、在驾驶员大脑与外部设备之间建立直接联系并实现信息交互至关重要。本文首先介绍了与脑电图相关的理论，然后回顾了脑电图在疲劳驾驶、分心驾驶和情绪驾驶等场景中的应用。已确定了现有研究的局限性，并提出了脑电图在未来脑机接口汽车辅助驾驶系统中的应用前景。这篇综述为研究人员利用脑电图提高驾驶安全性提供了指导。它还为未来的研究提供了有价值的建议。
