Toward Applicable EEG-Based Drowsiness Detection Systems: A Review

Abstract

Purpose: Drowsy driving accounts for many accidents and has attracted substantial research attention in recent years. Electroencephalography (EEG) signals are shown to be a reliable measure for the early detection of drowsiness. Unfortunately, there is no comprehensive study showing the applicability of drowsiness detection systems with EEG signals. In this research, we targeted the studies under the category of drowsiness detection, which adopted an EEG-based approach, to inspect the applicability of these systems from different aspects. Materials and Methods: We included documented studies that utilized clinical devices and consumer-grade EEG headsets for detection of drowsiness and investigated the selected studies from different aspects such as the number of EEG channels, sampling frequency, extracted features, type of classifiers, and accuracy of detection. Among available headsets, we focused on the most popular ones, namely Muse, NeuroSky, and EMOTIV brands. Results: Considerable number of studies have used EEG headsets, and their reports showed that the highest average accuracy belongs to EMOTIV, and the highest maximum detection accuracy, 98.8%, was achieved by the Muse headset. Spectral features extracted from short periods of 1, 2, or 10 secs are the most popular features, and the support vector machine is the most commonly used classifier in drowsiness detection systems. Therefore, implementing a reliable detection system does not necessarily include complicated features and classifiers. Conclusion: It is shown that, despite their few electrodes, commercial headsets have gained decent detection accuracy. This study sheds light on the current status of drowsiness detection systems and paves the way for future industrial designs of such systems.

Keywords

• Drowsiness Detection
• Fatigue
• Electroencephalography Features
• Commercial Electroencephalography Headsets
• Hybrid Systems