EEG-based System Using Deep Learning and Attention Mechanism for Driver Drowsiness Detection

Miankuan Zhu*, Haobo Li, Jiangfan Chen, Mitsuhiro Kamezaki, Zutao Zhang, Zexi Hua, Shigeki Sugano

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The lack of sleep (typically <6 hours a night) or driving for a long time are the reasons of drowsiness driving and caused serious traffic accidents. With pandemic of the COVID-19, drivers are wearing masks to prevent infection from it, which makes visual-based drowsiness detection methods difficult. This paper presents an EEG-based driver drowsiness estimation method using deep learning and attention mechanism. First of all, an 8-channels EEG collection hat is used to acquire the EEG signals in the simulation scenario of drowsiness driving and normal driving. Then the EEG signals are pre-processed by using the linear filter and wavelet threshold denoising. Secondly, the neural network based on attention mechanism and deep residual network (ResNet) is trained to classify the EEG signals. Finally, an early warning module is designed to sound an alarm if the driver is judged as drowsy. The system was tested under simulated driving environment and the drowsiness detection accuracy of the test set was 93.35%. Drowsiness warning simulation also verified the effectiveness of proposed early warning module.

Original languageEnglish
Title of host publication2021 IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages280-286
Number of pages7
ISBN (Electronic)9781665479219
DOIs
Publication statusPublished - 2021
Event32nd IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2021 - Nagoya, Japan
Duration: 2021 Jul 112021 Jul 17

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings

Conference

Conference32nd IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2021
Country/TerritoryJapan
CityNagoya
Period21/7/1121/7/17

ASJC Scopus subject areas

  • Computer Science Applications
  • Automotive Engineering
  • Modelling and Simulation

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