Literature DB >> 29031926

Monitoring driver fatigue using a single-channel electroencephalographic device: A validation study by gaze-based, driving performance, and subjective data.

José M Morales1, Carolina Díaz-Piedra2, Héctor Rieiro3, Joaquín Roca-González4, Samuel Romero5, Andrés Catena3, Luis J Fuentes6, Leandro L Di Stasi7.   

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.

Entities:  

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

Mesh:

Year:  2017        PMID: 29031926     DOI: 10.1016/j.aap.2017.09.025

Source DB:  PubMed          Journal:  Accid Anal Prev        ISSN: 0001-4575


  6 in total

1.  Intraocular pressure increases after complex simulated surgical procedures in residents: an experimental study.

Authors:  Jesús Vera; Carolina Diaz-Piedra; Raimundo Jiménez; Jose M Sanchez-Carrion; Leandro L Di Stasi
Journal:  Surg Endosc       Date:  2018-07-02       Impact factor: 4.584

2.  A Driver Gaze Estimation Method Based on Deep Learning.

Authors:  Sayyed Mudassar Shah; Zhaoyun Sun; Khalid Zaman; Altaf Hussain; Muhammad Shoaib; Lili Pei
Journal:  Sensors (Basel)       Date:  2022-05-23       Impact factor: 3.847

3.  EEG characteristic analysis of coach bus drivers based on brain connectivity as revealed via a graph theoretical network.

Authors:  Fuwang Wang; Xiaolei Zhang; Rongrong Fu; Guangbin Sun
Journal:  RSC Adv       Date:  2018-08-23       Impact factor: 4.036

4.  Microsaccades in Applied Environments: Real-World Applications of Fixational Eye Movement Measurements.

Authors:  Robert G Alexander; Stephen L Macknik; Susana Martinez-Conde
Journal:  J Eye Mov Res       Date:  2020-05-15       Impact factor: 0.957

5.  Real-Time ECG-Based Detection of Fatigue Driving Using Sample Entropy.

Authors:  Fuwang Wang; Hong Wang; Rongrong Fu
Journal:  Entropy (Basel)       Date:  2018-03-15       Impact factor: 2.524

6.  EEG Theta Power Activity Reflects Workload among Army Combat Drivers: An Experimental Study.

Authors:  Carolina Diaz-Piedra; María Victoria Sebastián; Leandro L Di Stasi
Journal:  Brain Sci       Date:  2020-03-28
  6 in total

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