Prediction of drowsiness using EEG signals in young Indonesian drivers

Maya Arlini Puspasari; Danu Hadi Syaifullah; Billy Muhamad Iqbal; Valda Aqila Afranovka; Safa Talitha Madani; Armand Khalif Susetyo; Salsabila Annisa Arista

doi:10.1016/j.heliyon.2023.e19499

Prediction of drowsiness using EEG signals in young Indonesian drivers

Heliyon. 2023 Sep 3;9(9):e19499. doi: 10.1016/j.heliyon.2023.e19499. eCollection 2023 Sep.

Authors

Maya Arlini Puspasari¹, Danu Hadi Syaifullah^{1

2}, Billy Muhamad Iqbal¹, Valda Aqila Afranovka¹, Safa Talitha Madani¹, Armand Khalif Susetyo¹, Salsabila Annisa Arista¹

Affiliations

¹ Department of Industrial Engineering, Universitas Indonesia, Indonesia.
² Centre for Business in Society, Coventry University, UK.

Abstract

Indonesia is among the countries with the highest accident rates in the world. Fatigue and drowsiness are among the main causes of the increased risks of accidents in the road transport sector. Sleep-related factors (quality and quantity, time of day) and work-related factors significantly affect the development of fatigue. The EEG signal indicator is often referred to as the gold standard for measuring fatigue and drowsiness. However, previous studies focused primarily on the trends of EEG signals under certain conditions but overlooking the development of drowsiness indicators based on EEG signals. Furthermore, existing studies still do not agree on what parameters in the EEG signal indicator are best at detecting drowsiness. Thus, this study aims to design an EEG signal-based drowsiness indicator under simulated driving conditions. Drowsy drivers were monitored through EEG signal indicators and subjective assessments. The methods used in this study include statistical significance tests, logistic regression, and support vector machine. The results showed that sleep deprivation had a significant effect on increasing alpha, beta, and theta waves. In addition, driving duration significantly increased the theta power and all EEG ratios and decreased the beta power in the alert group. The ratio of (θ + α)/β and θ/β in the SD group also showed a considerable increase in the end of driving. Furthermore, sleep status and driving duration both influenced subjective sleepiness. EEG signals combined with sleep status and driving duration factors generated acceptable model accuracies (77.1% and 90.2% in training and testing, respectively), with 90.5% sensitivity and 90% specificity in data test. Support vector machine showed better classification than that of logistics regression, with the linear kernel as the best classifier. Theta power had the highest effect in the model compared with other EEG signals.

Keywords: Drowsiness detection; EEG signals; Simulated driving; Sleep deprivation; Support vector machine.