Developing a Deep Neural Network for Driver Fatigue Detection Using EEG Signals Based on Compressed Sensing

Developing a Deep Neural Network for Driver Fatigue Detection Using EEG Signals Based on Compressed Sensing

In recent years, driver fatigue has become one of the main causes of road accidents. As a result, fatigue detection systems have been developed to warn drivers, and, among the available methods, EEG signal analysis is recognized as the most reliable method for detecting driver fatigue. This study pr...

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Bibliographic Details
Main Authors: Sobhan Sheykhivand, Tohid Yousefi Rezaii, Saeed Meshgini, Somaye Makoui, Ali Farzamnia
Format: Article
Language:English
English
Published: MDPI 2022
Subjects:
TA1-2040 Engineering (General). Civil engineering (General)
Online Access:https://eprints.ums.edu.my/id/eprint/32805/1/Developing%20a%20Deep%20Neural%20Network%20for%20Driver%20Fatigue%20Detection%20Using%20EEG%20Signals%20Based%20on%20Compressed%20Sensing.pdf
https://eprints.ums.edu.my/id/eprint/32805/2/Developing%20a%20Deep%20Neural%20Network%20for%20Driver%20Fatigue%20Detection%20Using%20EEG%20Signals%20Based%20on%20Compressed%20Sensing1.pdf

Internet

https://eprints.ums.edu.my/id/eprint/32805/1/Developing%20a%20Deep%20Neural%20Network%20for%20Driver%20Fatigue%20Detection%20Using%20EEG%20Signals%20Based%20on%20Compressed%20Sensing.pdf
https://eprints.ums.edu.my/id/eprint/32805/2/Developing%20a%20Deep%20Neural%20Network%20for%20Driver%20Fatigue%20Detection%20Using%20EEG%20Signals%20Based%20on%20Compressed%20Sensing1.pdf

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