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A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness

Department of Electronic Engineering, Pukyong National University, Busan 608-737, Korea
Author to whom correspondence should be addressed.
Academic Editor: Xue Wang
Sensors 2015, 15(8), 20873-20893;
Received: 6 July 2015 / Revised: 30 July 2015 / Accepted: 18 August 2015 / Published: 21 August 2015
(This article belongs to the Special Issue Wearable Sensors)
PDF [1567 KB, uploaded 21 August 2015]


Driver 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
Keywords: driver drowsiness detection; EEG; gyroscope; slightly drowsy events; mobile application driver drowsiness detection; EEG; gyroscope; slightly drowsy events; mobile application

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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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.

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