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

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2021 IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ280-286
ページ数7
ISBN(電子版)9781665479219
DOI
出版ステータスPublished - 2021
イベント32nd IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2021 - Nagoya, Japan
継続期間: 2021 7月 112021 7月 17

出版物シリーズ

名前IEEE Intelligent Vehicles Symposium, Proceedings

Conference

Conference32nd IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2021
国/地域Japan
CityNagoya
Period21/7/1121/7/17

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 自動車工学
  • モデリングとシミュレーション

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