AGL-Net: An Efficient Neural Network for EEG-Based Driver Fatigue Detection.

Journal: Journal of integrative neuroscience
Published Date:

Abstract

BACKGROUND: In recent years, road traffic safety has become a prominent issue due to the worldwide proliferation of vehicles on roads. The challenge of driver fatigue detection involves balancing the efficiency and accuracy of the detection process. While various detection methods are available, electroencephalography (EEG) is considered the gold standard due to its high precision in terms of detecting fatigue. However, deep learning models for EEG-based fatigue detection are limited by their large numbers of parameters and low computational efficiency levels, making it difficult to implement them on mobile devices.

Authors

  • Weijie Fang
    School of Software, South China Normal University, 528200 Foshan, Guangdong, China.
  • Liren Tang
    School of Software, South China Normal University, 528200 Foshan, Guangdong, China.
  • Jiahui Pan
    School of Software, South China Normal University, Guangzhou 510641, China.