Citation

Akbar IA, Rumagit AM, Utsunomiya M, Morie T, Igasaki T. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2017; 2017: 2904-2907.

Copyright

(Copyright © 2017, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/EMBC.2017.8037464

PMID

29060505

Abstract

Traffic accidents remain one of the most critical issues in many countries. One of the major causes of traffic accidents is drowsiness while driving. Since drowsiness is related to human physiological conditions, drowsiness is hard to prevent. Several studies have been conducted in assessing drowsiness, especially in a driving environment. One of the common methods used is the electroencephalogram (EEG). It is known that drowsiness occurs in the central nervous system; thus, estimating drowsiness using EEG is the promising way to assess drowsiness accurately. In this study, we tried to estimate drowsiness using frequency-domain and time-domain analysis of EEG. To validate the physiological conditions of the subjects, the Karolinska sleepiness scale (KSS), a subject-based assessment of drowsiness condition; and an examiner-based assessment known as facial expression evaluation (FEE) were applied. Three categories were considered; alert (KSS <; 6; FEE <; 1), weak drowsiness (KSS 6-7; FEE 1-2) and strong drowsiness (KSS > 7; FEE > 2). The six parameters (absolute and relative power of alpha, ratio of β/α and (θ+α)/β, and Hjorth activity and mobility parameters) had statistically significant differences between the three drowsiness conditions (P <; 0.001). By using both KSS and FEE, these parameters showed high accuracy in detecting drowsiness (up to 92.9%). Taken together, we suggest that EEG parameters can be used in detecting the three drowsiness conditions in a simulated driving environment.

Language: en