  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.
