Analysis of EEG detection of driver active fatigue on expressway

doi:10.16265/j.cnki.issn1003-3033.2018.10.003

Abstract

Abstract: To develop a system for warning the expressway driving fatigue and ensure road traffic safety, this study was aimed at exploring the relationship between EEG indicator (θ+α)/β and driver's active fatigue based on EEG data power spectrum analysis. EEG indicator (θ+α)/β was calculated by assessing three frequency bands: alpha (8-13 Hz), beta (13-30 Hz) and theta (4-8 Hz), during a simulated driving session in 21 subjects. SOFI scale was adopted to analyse fitigue state before and after driving, and regression method was used to examine the fitting validity of psychological measures and EEG algorithm (θ+α)/β.The results show that there is a negative correlation between the (θ+α)/β activity and time, that there is a positive correlation between EEG indicator (θ+α)/β and the driver's active fatigue, and the fitting interpretation rate is 50%, and that the EEG indicator (θ+α)/β is able to predict active fatigue in real time.

Key words: simulated driving, expressway, active fatigue, electroencephalogram(EEG), swedish occupational fatigue inventory questionnaire (SOFI)

CLC Number:

X912

LIU Tianjiao, MA Jinfei. Analysis of EEG detection of driver active fatigue on expressway[J]. China Safety Science Journal, 2018, 28(10): 13-18.

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