Naslov
A Review of EEG Signal Features and their Application in Driver Drowsiness Detection Systems

Autori
Stančin, Igor ; Cifrek, Mario ; Jović, Alan

Izvornik
Sensors (1424-8220) 21 (2021), 11; 3786, 29

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, pregledni rad, znanstveni

Ključne riječi
drowsiness detection ; EEG features ; feature extraction ; machine learning ; drowsiness classification ; fatigue detection ; deep learning

Sažetak
Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system. In this context, electroencephalogram (EEG) signals are the most important source of data to achieve successful detection. In this paper, we first review EEG signal features used in the literature for a variety of tasks, then we focus on reviewing the applications of EEG features and deep learning approaches in driver drowsiness detection, and finally we discuss the open challenges and opportunities in improving driver drowsiness detection based on EEG. We show that the number of studies on driver drowsiness detection systems has increased in recent years and that future systems need to consider the wide variety of EEG signal features and deep learning approaches to increase the accuracy of detection.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Kognitivna znanost (prirodne, tehničke, biomedicina i zdravstvo, društvene i humanističke znanosti)

POVEZANOST RADA