TY - GEN
T1 - EEG-based System Using Deep Learning and Attention Mechanism for Driver Drowsiness Detection
AU - Zhu, Miankuan
AU - Li, Haobo
AU - Chen, Jiangfan
AU - Kamezaki, Mitsuhiro
AU - Zhang, Zutao
AU - Hua, Zexi
AU - Sugano, Shigeki
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China under Grant No. 51975490, by the Research Institute for Science and Engineering, Waseda University, by the National Key Research and Development Project of China under Grant No. 2020YFB1711902, by the Science and Technology Projects of Sichuan under Grant No. 2020YFSY0070, 2021JDRC0096, and by the China Scholarship Council. This work was presented at the WS18, In-cabin human-sensing and interaction in intelligent vehicles (HSIV), IV2021.
Funding Information:
ACKNOWLEDGMENT This work was supported by the National Natural Science Foundation of China under Grant No. 51975490, by the Research Institute for Science and Engineering, Waseda University, by the National Key Research and Development Project of China under Grant No. 2020YFB1711902, by the Science and Technology Projects of Sichuan under Grant No. 2020YFSY0070, 2021JDRC0096, and by the China Scholarship Council. This work was presented at the WS18, In-cabin human-sensing and interaction in intelligent vehicles (HSIV), IV2021.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
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U2 - 10.1109/IVWorkshops54471.2021.9669234
DO - 10.1109/IVWorkshops54471.2021.9669234
M3 - Conference contribution
AN - SCOPUS:85124965447
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 280
EP - 286
BT - 2021 IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2021
Y2 - 11 July 2021 through 17 July 2021
ER -
