Citation

Morales JM, Diaz-Piedra C, Rieiro H, Roca-González J, Romero S, Catena A, Fuentes LJ, Di Stasi LL. Accid. Anal. Prev. 2017; 109: 62-69.

Affiliation

Mind, Brain, and Behavior Research Center, University of Granada, Granada, Spain; College of Nursing and Health Innovation, Arizona State University, Phoenix, AZ, USA. Electronic address: distasi@ugr.es.

Copyright

(Copyright © 2017, Elsevier Publishing)

DOI

10.1016/j.aap.2017.09.025

PMID

29031926

Abstract

Driver fatigue can impair performance as much as alcohol does. It is the most important road safety concern, causing thousands of accidents and fatalities every year. Thanks to technological developments, wearable, single-channel EEG devices are now getting considerable attention as fatigue monitors, as they could help drivers to assess their own levels of fatigue and, therefore, prevent the deterioration of performance. However, the few studies that have used single-channel EEG devices to investigate the physiological effects of driver fatigue have had inconsistent results, and the question of whether we can monitor driver fatigue reliably with these EEG devices remains open. Here, we assessed the validity of a single-channel EEG device (TGAM-based chip) to monitor changes in mental state (from alertness to fatigue). Fifteen drivers performed a 2-h simulated driving task while we recorded, simultaneously, their prefrontal brain activity and saccadic velocity. We used saccadic velocity as the reference index of fatigue. We also collected subjective ratings of alertness and fatigue, as well as driving performance. We found that the power spectra of the delta EEG band showed an inverted U-shaped quadratic trend (EEG power spectra increased for the first hour and half, and decreased during the last thirty minutes), while the power spectra of the beta band linearly increased as the driving session progressed. Coherently, saccadic velocity linearly decreased and speeding time increased, suggesting a clear effect of fatigue. Subjective data corroborated these conclusions. Overall, our results suggest that the TGAM-based chip EEG device is able to detect changes in mental state while performing a complex and dynamic everyday task as driving.

Copyright © 2017 Elsevier Ltd. All rights reserved.

Language: en

Keywords

Brain activity; Driving simulation; Eye movements; Fatigue detector; Low-cost technology; Wearable technology