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Drivers cognitive and physiological states affect their ability to controltheir vehicles. Thus, these driver states are important to the safety ofautomobiles. The design of advanced driver assistance systems (ADAS) orautonomous vehicles will depend on their ability to interact effectively withthe driver. A deeper understanding of the driver state is, therefore,paramount. EEG is proven to be one of the most effective methods for driverstate monitoring and human error detection. This paper discusses EEG-baseddriver state detection systems and their corresponding analysis algorithms overthe last three decades. First, the commonly used EEG system setup for driverstate studies is introduced. Then, the EEG signal preprocessing, featureextraction, and classification algorithms for driver state detection arereviewed. Finally, EEG-based driver state monitoring research is reviewedin-depth, and its future development is discussed. It is concluded that thecurrent EEG-based driver state monitoring algorithms are promising for safetyapplications. However, many improvements are still required in EEG artifactreduction, real-time processing, and between-subject classification accuracy.

A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis