Wireless and Wearable EEG System for Evaluating Driver Vigilance

警惕(心理学) 脑电图 可穿戴计算机 计算机科学 神经生理学 唤醒 无线 驾驶模拟器 模拟 心理学 嵌入式系统 认知心理学 电信 精神科 神经科学
作者
Chin‐Teng Lin,Chun‐Hsiang Chuang,Chih-Sheng Huang,Shu-Fang Tsai,Shao-Wei Lu,Yen-Hsuan Chen,Li‐Wei Ko
出处
期刊:IEEE Transactions on Biomedical Circuits and Systems [Institute of Electrical and Electronics Engineers]
卷期号:8 (2): 165-176 被引量:233
标识
DOI:10.1109/tbcas.2014.2316224
摘要

Brain activity associated with attention sustained on the task of safe driving has received considerable attention recently in many neurophysiological studies. Those investigations have also accurately estimated shifts in drivers' levels of arousal, fatigue, and vigilance, as evidenced by variations in their task performance, by evaluating electroencephalographic (EEG) changes. However, monitoring the neurophysiological activities of automobile drivers poses a major measurement challenge when using a laboratory-oriented biosensor technology. This work presents a novel dry EEG sensor based mobile wireless EEG system (referred to herein as Mindo) to monitor in real time a driver's vigilance status in order to link the fluctuation of driving performance with changes in brain activities. The proposed Mindo system incorporates the use of a wireless and wearable EEG device to record EEG signals from hairy regions of the driver conveniently. Additionally, the proposed system can process EEG recordings and translate them into the vigilance level. The study compares the system performance between different regression models. Moreover, the proposed system is implemented using JAVA programming language as a mobile application for online analysis. A case study involving 15 study participants assigned a 90 min sustained-attention driving task in an immersive virtual driving environment demonstrates the reliability of the proposed system. Consistent with previous studies, power spectral analysis results confirm that the EEG activities correlate well with the variations in vigilance. Furthermore, the proposed system demonstrated the feasibility of predicting the driver's vigilance in real time.
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