Driver drowsiness is one of the main causes of road accidents. It is a serious hazard in transportation systems for drivers’ safety. There are various symptoms that indicate to the drivers to stop driving and take a rest; e.g., heavy eyelids, difficulty in focusing, missing traffic signs, yawning, trouble keeping the head up, feeling restless and irritable, drifting from lane, blurry vision, rubbing eyes, etc. However, avoiding these signs could lead to a serious or fatal accident while driving.6 It has been reported that sleep- related casualties are a major issue in the transport system7,8 as well as for the automobile industry.

Different drivers’ drowsiness detection metrics have been proposed in the literature that have focused on lane deviations, high level of fatigue, video-based system (detecting eyes closing or eye blinks).9-12 The reported methods in the literature are based on various measurements including subjective measure, vehicle-based measure, behavior measure, and physiological measure. However, no perfect alert system for drivers’ drowsiness detection exists, so each proposed system has limitations in terms of cost, effectiveness, flexibility, or practical implementation.

The use of neuroimaging techniques such as EEG can help the researchers to test for drowsy drivers in a simulated experimental environment, since it is not safe or ethical to allow a drowsy driver on the road for an experiment. Thus, the simulated environment helps the researchers to control the costs, efficiency, safety, and easy-to-collect data. In addition, the use of EEG signals can record the specific pattern that changes with the sleepy state or loss of alertness. Therefore, in future an EEG-based alert system may be introduced by the automobile companies for informing the drowsy drivers either by sound alarm or direct controlling of the vehicle.

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