Wireless and wearable EEG system for evaluating driver vigilance

Chin Teng Lin, Chun Hsiang Chuang, Chih Sheng Huang, Shu Fang Tsai, Shao Wei Lu, Yen Hsuan Chen, Li-Wei Ko*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

123 Scopus citations

Abstract

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.

Original languageEnglish
Article number6818437
Pages (from-to)165-176
Number of pages12
JournalIEEE Transactions on Biomedical Circuits and Systems
Volume8
Issue number2
DOIs
StatePublished - 1 Jan 2014

Keywords

  • Brain computer interface
  • dry electroencephalographic (EEG) system
  • machine learning
  • vigilance monitoring

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