3D-STCNN: Spatiotemporal Convolutional Neural Network Based on EEG 3D Features for Detecting Driving Fatigue

Authors

  • Bo Peng School of Computer Science, Chengdu University of Information Technology, and School of Software, Sichuan Vocational College of Information Technology, China
  • Dongrui Gao School of Computer Science, Chengdu University of Information Technology, and School of Life Sciences and Technology, University of Electronic Science and Technology of China, China
  • Manqing Wang School of Computer Science, Chengdu University of Information Technology, and School of Life Sciences and Technology, University of Electronic Science and Technology of China, China
  • Yongqing Zhang School of Computer Science, Chengdu University of Information Technology, China https://orcid.org/0000-0003-3422-8305

DOI:

https://doi.org/10.47852/bonviewJDSIS3202983

Keywords:

fatigue testing, electroencephalogram, feature extraction, deep learning

Abstract

Fatigue driving has become one of the main causes of traffic accidents, and driving fatigue detection based on electroencephalogram (EEG) can effectively evaluate the driver’s mental state and avoid the occurrence of traffic accidents. This article evaluates a feature extraction method for extracting multiple features of EEG signals and establishes a spatiotemporal convolutional neural network (STCNN) to detect driver fatigue. Firstly, we constructed a three-dimensional feature of the EEG signal, which includes the frequency domain, time domain, and spatial features of the EEG signal. Then, we use STCNN for fatigue state classification. STCNN is composed of an attention time network based on attention mechanism and an attention convolutional neural network based on attention mechanism. In addition, we conducted fatigue driving experiments and collected EEG signals from 14 subjects in both awake and fatigued states, ultimately collecting EEG data under three different driving task loads. We conducted extensive experiments on this basis and compared the effectiveness of STCNN and six competitive methods. The results show that the classification accuracy of STCNN is 87.55%, which can effectively detect the fatigue status of drivers.

 

Received: 22 April 2023 | Revised: 15 June 2023 | Accepted: 26 June 2023

 

Conflicts of Interest:

Yongqing Zhang is an associate editor for Journal of Data Science and Intelligent Systems, and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work.

 

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

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Published

2023-06-26

How to Cite

Peng, B., Gao, D., Wang, M., & Zhang, Y. (2023). 3D-STCNN: Spatiotemporal Convolutional Neural Network Based on EEG 3D Features for Detecting Driving Fatigue. Journal of Data Science and Intelligent Systems, 2(1). https://doi.org/10.47852/bonviewJDSIS3202983

Issue

Section

Research Articles