Automotive Innovation ›› 2023, Vol. 6 ›› Issue (1): 20-31.doi: 10.1007/s42154-022-00206-z

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Drivers’ EEG Responses to Different Distraction Tasks

Guofa Li1 · Xiaojian Wu2 · Arno Eichberger3 · Paul Green4 · Cristina Olaverri‑Monreal5 · Weiquan Yan2#br#  ·#br# Yechen Qin6  · Yuezhi Li7   

  1. 1. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
    2. Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen
    University, Shenzhen 518060, China
    3. Institute of Automotive Engineering, Graz University of Technology, 8010 Graz, Austria
    4. University of Michigan Transportation Research Institute and Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109, USA
    5. Chair ITS-Sustainable Transport Logistics 4.0, Johannes Kepler University, 4040 Linz, Austria
    6. Department of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
    7. Laboratory of Neural Engineering, College of Mechatronics and Control Engineering, Shenzhen University,
    Shenzhen 518060, China
  • Online:2023-03-06 Published:2023-03-06

Abstract: Driver distraction has been deemed a major cause of traffic accidents. However, drivers’ brain response activities to different distraction types have not been well investigated. The purpose of this study is to investigate the response of electroencephalography (EEG) activities to different distraction tasks. In the conducted simulation tests, three secondary tasks (i.e., a clock task, a 2-back task, and a navigation task) are designed to induce different types of driver distractions. Twenty-four participants are recruited for the designed tests, and differences in drivers’ brain response activities concerning distraction types are investigated. The results show that the differences in comprehensive distraction are more significant than that in single cognitive distraction. Friedman test and post hoc two-tailed Nemenyi test are conducted to further identify the differences in band activities among brain regions. The results show that the theta energy in the frontal lobe is significantly higher than that in other brain regions in distracted driving, whereas the alpha energy in the temporal lobe significantly decreases compared to other brain regions. These results provide theoretical references for the development of distraction detection systems based on EEG signals.

Key words: Driving safety, , Driver distraction, , EEG, , Autonomous vehicle