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...
| Autori principali: | , , , , |
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| Natura: | Articolo |
| Lingua: | English English |
| Pubblicazione: |
MDPI
2022
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| Soggetti: | |
| Accesso online: | 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 |
Accesso online
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.pdfhttps://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