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

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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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Authors and Affiliations

  1. Technology and Medical Imaging Laboratory, Faculty of Medicine Monastir, University of Monastir, 5019, Monastir, Tunisia

    Afef Abidi, Khaled Ben Khalifa, Ridha Ben Cheikh & Mohamed Hedi Bedoui

  2. National Engineering School of Sousse, University of Sousse, BP 264 Erriyadh, 4023, Sousse, Tunisia

    Afef Abidi

  3. Department of Electronics and Microelectronics (SEMi), University of Mons, 7000, Mons, Belgium

    Afef Abidi & Carlos Alberto Valderrama Sakuyama

  4. Higher Institute of Applied Science and Technology of Sousse, University of Sousse, 4003, Sousse, Tunisia

    Khaled Ben Khalifa

  5. Service d’explorations fonctionnelles du systeme nerveux, CHU Sahloul, 4054, Sousse, Tunisia

    Ridha Ben Cheikh

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  1. Afef Abidi
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Correspondence to Khaled Ben Khalifa.

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Abidi, A., Ben Khalifa, K., Ben Cheikh, R. et al. Automatic Detection of Drowsiness in EEG Records Based on Machine Learning Approaches. Neural Process Lett 54, 5225–5249 (2022). https://doi.org/10.1007/s11063-022-10858-x

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