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Article

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

by 1,2,†, 1,2,†, 1,2, 3, 3,4,*,‡ and 5,‡
1
Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACL), University of Sfax, Sfax 3021, Tunisia
2
Digital Research Center of Sfax, B.P. 275, Sakiet Ezzit, Sfax 3021, Tunisia
3
Institute of Sport Science, Otto-von-Guericke University Magdeburg, 39104 Magdeburg, Germany
4
Interdisciplinary Laboratory in Neurosciences, Physiology and Psychology: Physical Activity, Health and Learning (LINP2), UFR STAPS, UPL, Paris Nanterre University, 92000 Nanterre, France
5
IRIT-ENSEEIHT, University of Toulouse, 31013 Toulouse, France
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work as first author.
These authors contributed equally to this work as last author.
Academic Editor: Giovanni Sparacino
Sensors 2021, 21(5), 1734; https://doi.org/10.3390/s21051734
Received: 5 December 2020 / Revised: 4 February 2021 / Accepted: 24 February 2021 / Published: 3 March 2021
Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures; (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works. View Full-Text
Keywords: drowsiness detection; EEG signals; Emotiv EPOC+; deep learning; data augmentation; convolutional neural networks; classification; awake/drowsy states drowsiness detection; EEG signals; Emotiv EPOC+; deep learning; data augmentation; convolutional neural networks; classification; awake/drowsy states
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MDPI and ACS Style

Chaabene, S.; Bouaziz, B.; Boudaya, A.; Hökelmann, A.; Ammar, A.; Chaari, L. Convolutional Neural Network for Drowsiness Detection Using EEG Signals. Sensors 2021, 21, 1734. https://doi.org/10.3390/s21051734

AMA Style

Chaabene S, Bouaziz B, Boudaya A, Hökelmann A, Ammar A, Chaari L. Convolutional Neural Network for Drowsiness Detection Using EEG Signals. Sensors. 2021; 21(5):1734. https://doi.org/10.3390/s21051734

Chicago/Turabian Style

Chaabene, Siwar, Bassem Bouaziz, Amal Boudaya, Anita Hökelmann, Achraf Ammar, and Lotfi Chaari. 2021. "Convolutional Neural Network for Drowsiness Detection Using EEG Signals" Sensors 21, no. 5: 1734. https://doi.org/10.3390/s21051734

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