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

• 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; https://eprints.ums.edu.my/id/eprint/32805/; https://www.mdpi.com/2071-1050/14/5/2941; https://doi.org/10.3390/su14052941

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...