Determination of optimal electroencephalography recording locations for detecting drowsy driving-Reference-Cited by-同舟云学术

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Determination of optimal electroencephalography recording locations for detecting drowsy driving

Published:2018-03-06 Issue:5 Volume:12 Page:345-350
ISSN:1751-9578
Container-title:IET Intelligent Transport Systems
language:en
Short-container-title:IET Intelligent Transport Systems

Author:

Zhang Chaofei¹,Wang Wenjun¹,Chen Chaoyang²,Zeng Chao³,Anderson Dennis E.⁴⁵,Cheng Bo¹

Affiliation:

1. Department of Automotive EngineeringTsinghua UniversityBeijing100084People's Republic of China

2. Department of Biomedical EngineeringWayne State UniversityDetroitMI48201USA

3. College of Information Science and Technology, Shihezi UniversityShihezi832000People's Republic of China

4. Beth Israel Deaconess Medical CenterBostonUSA

5. Harvard Medical SchoolBostonUSA

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Law,Mechanical Engineering,General Environmental Science,Transportation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-its.2017.0083

Reference36 articles.

1. https://sleepfoundation.org/sites/default/files/2009%20SLEEP%20IN%20AMERICA%20SOF%20EMBARGOED_0.PDF accessed November 2017

2. Driver drowsiness detection based on multisource information

3. EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks;Berka C.;Aviat. Space Environ. Med.,2007

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1. Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea;Sensors;2024-04-19

2. Effect of psychoacoustic annoyance on EEG signals of tractor drivers;Archives of Acoustics;2023-07-26

3. Automatic Detection of Drowsiness in EEG Records Based on Machine Learning Approaches;Neural Processing Letters;2022-05-25

4. Estimating the Vigilance of High-Speed Rail Drivers Using a Stacking Ensemble Learning Method;IEEE Sensors Journal;2021-08-01

5. Driver Monitoring System Based on CNN Models: An Approach for Attention Level Detection;Lecture Notes in Computer Science;2020

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