Driver Distraction From the EEG Perspective: A Review

A large proportion of car accidents are caused by distracted drivers. Thus, comprehensive analysis and understanding on driver distraction is essential for traffic safety improvement. Driver distraction can be revealed from their facial expression in images. However, this is easily affected by complex light distribution on faces or by low illumination during nighttime. Differently, drivers’ physiological signals, such as electroencephalography (EEG), have been convinced to be one of the most reliable and direct tools for driver distraction studies, either for deeper understanding on driver distraction or for effective detection of driver distraction. Therefore, this article comprehensively reviews multiple aspects of driver distraction from the EEG perspective. First, the research progress on distracted driving is reviewed from three aspects: the definition of distraction, the types of distraction, and the main datasets of distracted driving. Second, computer signal processing is summarized into four aspects: signal acquisition, signal pretreatment, EEG main frequency bands, and EEG characteristics, and analyzed in turn. Third, the variation trends of EEG frequency bands under different distraction types were analyzed and compared. Fourth, the methods of feature extraction and detection of EEG in distracted driving are reviewed from the perspective of methodology. Finally, a new distraction detection method based on EEG integration with other physiological signals is summarized, and future development trends and technical challenges are prospected.