An Identification Method for Road Hypnosis Based on Human EEG Data.

Journal: Sensors (Basel, Switzerland)
Published Date: Jul 6, 2024

Abstract

The driver in road hypnosis has not only some external characteristics, but also some internal characteristics. External features have obvious manifestations and can be directly observed. Internal features do not have obvious manifestations and cannot be directly observed. They need to be measured with specific instruments. Electroencephalography (EEG), as an internal feature of drivers, is the golden parameter for drivers' life identification. EEG is of great significance for the identification of road hypnosis. An identification method for road hypnosis based on human EEG data is proposed in this paper. EEG data on drivers in road hypnosis can be collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with the PSD (power spectral density) method, and EEG characteristics are extracted. The neural networks EEGNet, RNN, and LSTM are used to train the road hypnosis identification model. It is shown from the results that the model based on EEGNet has the best performance in terms of identification for road hypnosis, with an accuracy of 93.01%. The effectiveness and accuracy of the identification for road hypnosis are improved in this study. The essential characteristics for road hypnosis are also revealed. This is of great significance for improving the safety level of intelligent vehicles and reducing the number of traffic accidents caused by road hypnosis.

Authors

  • Bin Wang
    State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China; New South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga 2650, Australia. Electronic address: bin.a.wang@dpi.nsw.gov.au.
  • Jingheng Wang
    Department of Mathematics, Ohio State University, Columbus, OH 43220, USA.
  • Xiaoyuan Wang
    College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao, China.
  • Longfei Chen
    College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao, China.
  • Han Zhang
    Johns Hopkins University, Baltimore, MD, USA.
  • Chenyang Jiao
    College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.
  • Gang Wang
    National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
  • Kai Feng
    College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.

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