title: A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness.
abstract: 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.
authors: Department of Electronic Engineering, Pukyong National University, Busan 608-737, Korea. ligang@pknu.ac.kr.
authors: Gang
authors: G
authors: Li
authors: Department of Electronic Engineering, Pukyong National University, Busan 608-737, Korea. wychung@pknu.ac.kr.
authors: Wan-Young
authors: WY
authors: Chung
mesh_terms: D001334:Automobile Driving; D001364:Awareness; D004566:Electrodes; D004569:Electroencephalography / Q000295:instrumentation*; D006801:Humans; D012815:Signal Processing, Computer-Assisted; D012894:Sleep Stages; D000068997:Smartphone
keywords: EEG; driver drowsiness detection; gyroscope; mobile application; slightly drowsy events