A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness
AbstractDriver drowsiness is a major cause of mortality in traffic accidents worldwide. Electroencephalographic (EEG) signal, which reflects the brain activities, is more directly related to drowsiness. Thus, many Brain-Machine-Interface (BMI) systems have been proposed to detect driver drowsiness. However, detecting driver drowsiness at its early stage poses a major practical hurdle when using existing BMI systems. This study proposes a context-aware BMI system aimed to detect driver drowsiness at its early stage by enriching the EEG data with the intensity of head-movements. The proposed system is carefully designed for low-power consumption with on-chip feature extraction and low energy Bluetooth connection. Also, the proposed system is implemented using JAVA programming language as a mobile application for on-line analysis. In total, 266 datasets obtained from six subjects who participated in a one-hour monotonous driving simulation experiment were used to evaluate this system. According to a video-based reference, the proposed system obtained an overall detection accuracy of 82.71% for classifying alert and slightly drowsy events by using EEG data alone and 96.24% by using the hybrid data of head-movement and EEG. These results indicate that the combination of EEG data and head-movement contextual information constitutes a robust solution for the early detection of driver drowsiness. View Full-Text
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Li, G.; Chung, W.-Y. A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness. Sensors 2015, 15, 20873-20893.
Li G, Chung W-Y. A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness. Sensors. 2015; 15(8):20873-20893.Chicago/Turabian Style
Li, Gang; Chung, Wan-Young. 2015. "A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness." Sensors 15, no. 8: 20873-20893.