Drowsiness detection using portable wireless EEG

doi:10.1016/j.cmpb.2021.106535

2022-02-01

PRE

Sagila Gangadharan K. *

A. P. Vinod

DOI：10.1016/j.cmpb.2021.106535

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Background and Objective: The ever-increasing fatality rate due to traffic and workplace accidents, resulting from drowsiness have been a persistent concern during the past years. An efficient technology capable of monitoring and detecting drowsiness can help to alleviate this concern and has potential applications in driver vigilance monitoring, vigilance monitoring in air traffic control rooms and other safety critical work places. In this paper, we present the feasibility of a wearable light weight wireless consumer grade Electroencephalogram (EEG)-based drowsiness detection. Methods: A set of informative features were extracted from short daytime nap EEG signals and their applicability in discriminating between alert and drowsy state was studied. We derived an optimal set of EEG features, that give maximum detection rate for the drowsy state. In addition, heart rate was also recorded concurrently with EEG and correlation between heart rate and the EEG features corresponding to drowsiness was also studied. Results: Using the selected features, the EEG data is shown to be capable of classifying alert and drowsy states with an accuracy of 78.3% using Support Vector Machine classifier employing cross subject validation. The feature selection results also revealed that, the EEG features extracted from the temporal electrodes are more significant for drowsiness detection than the features from frontal electrodes. In addition, EEG features extracted from the temporal electrodes yielded higher correlation coefficient with heart rate, which was in concordance with the feature selection results. Conclusions: The results reveal that using the proposed drowsiness detection algorithm, it is possible to perform drowsiness detection using a single EEG electrode placed behind the ear. (C) 2021 Elsevier B.V. All rights reserved.

Keyword:

Drowsiness detection

Electroencephalogram

Heart rate correlation

Support vector machine

Cross subject validation