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Electroencephalogram (EEG) is used to analyze the state of the brain. One of the critical states of the brain is drowsiness. Physical, mental tiredness, and unconsciousness are some of the reasons for drowsiness. Drowsiness state may lead to fatal crashes, severe injury, and property damage; sometimes, it can be analyzed and detected by using EEG. Analyzing EEG signals is complicated and tedious, so an automated diagnosis is required to interpret these signals effectively. In recent years, finding drowsy feeling while working has become an important research area. In this paper, the authors reviewed various drowsiness detection techniques in the literature and analyzed the performance of 15 different machine learning algorithms for the self-acquired feature set from the EEG.

Deep Review of Machine Learning Techniques on Detection of Drowsiness Using EEG Signal