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Neural Processing Letters
Published in: Neural Processing Letters 6/2022

25-05-2022

Automatic Detection of Drowsiness in EEG Records Based on Machine Learning Approaches

Authors: Afef Abidi, Khaled Ben Khalifa, Ridha Ben Cheikh, Carlos Alberto Valderrama Sakuyama, Mohamed Hedi Bedoui

Published in: Neural Processing Letters | Issue 6/2022

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Abstract

Drowsy driving is a major cause of road accidents. Traffic accidents can be prevented by discriminating between driver states of alertness and drowsiness. This paper presents an efficient system for drowsiness detection based on EEG signals. The proposed system is efficient in providing consistent results regardless of the inherent characteristics of drivers. Our method is based on features extracted from well-defined sub-bands. These sub-bands obtained using a tunable Q-factor wavelet transform. The use of sub-bands solves the problem of interpersonal variability of EEG recordings, which is a major problem in detecting drowsiness. In addition, the use of kernel principal component analysis reduces the size of the features extracted from EEG signals without degrading the accuracy. Indeed, a single differential EEG channel with a minimal number of carefully selected features is sufficient to provide a fast, convenient, and accurate detection system. For drowsiness recognition, two different machine learning techniques, K-nearest neighbours and support vector machines, are proposed. The latter consists of a learning module for medical diagnosis based on EEG signals from a set of laboratory subjects. Laboratory conditions help identify characteristic and common features. These preparatory parameters make it possible to provide a real-time adaptive drowsiness diagnosis by assessing the driver's condition every second. By customizing the system, it can detect drowsiness with an accuracy of approximately 94%.
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Metadata
Title
Automatic Detection of Drowsiness in EEG Records Based on Machine Learning Approaches
Authors
Afef Abidi
Khaled Ben Khalifa
Ridha Ben Cheikh
Carlos Alberto Valderrama Sakuyama
Mohamed Hedi Bedoui
Publication date
25-05-2022
Publisher
Springer US
Published in
Neural Processing Letters / Issue 6/2022
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-10858-x

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