Fatigue Driving Detection of EEG Signals by LSTM Deep Neural Network with LPSD and DE-Reference-Cited by-同舟云学术

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Fatigue Driving Detection of EEG Signals by LSTM Deep Neural Network with LPSD and DE

Published:2023-08-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Mechatronics and Automation (ICMA)
language:
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Author:

Zhang Hao¹,Dong Enzeng¹,Tong Jigang¹,Yang Sen¹,Du Shengzhi²

Affiliation:

1. Tianjin University of Technology 391 Binshui West Road,Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory,Tianjin,China

2. Tshwane University of Technology,Department of Electrical Engineering,Pretoria,South Africa,0001

Funder

National Natural Science Foundation of China

National Research Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10215380/10215516/10216090.pdf?arnumber=10216090

Reference27 articles.

1. The impact of misclassification due to survey response fatigue on estimation and identifiability of treatment effects

2. Fatigue assessment using ECG and actigraphy sensors

3. EEG signal analysis for the assessment and quantification of driver’s fatigue

4. Design and Implementation of a Drowsiness-Fatigue-Detection System Based on Wearable Smart Glasses to Increase Road Safety

5. Surface EMG based muscle fatigue evaluation in biomechanics

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