Convolutional Neural Network for Drowsiness Detection Using EEG Signals
Identifieur interne : 000800 ( Main/Exploration ); précédent : 000799; suivant : 000801Convolutional Neural Network for Drowsiness Detection Using EEG Signals
Auteurs : Siwar Chaabene [Tunisie] ; Bassem Bouaziz [Tunisie] ; Amal Boudaya [Tunisie] ; Anita Hökelmann ; Achraf Ammar [France] ; Lotfi ChaariSource :
- Sensors (Basel, Switzerland) [ 1424-8220 ] ; 2021.
Descripteurs français
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- MESH :
English descriptors
- KwdEn :
- MESH :
Abstract
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
Url:
DOI: 10.3390/s21051734
PubMed: 33802357
PubMed Central: 7959292
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>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 <italic>Emotiv EPOC<inline-formula><mml:math id="mm1"><mml:mrow><mml:msup><mml:mrow></mml:mrow>
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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.</p>
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<author><name sortKey="Makeig, S" uniqKey="Makeig S">S. Makeig</name>
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<author><name sortKey="Wilson, N" uniqKey="Wilson N">N. Wilson</name>
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<author><name sortKey="Montgomery, L" uniqKey="Montgomery L">L. Montgomery</name>
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</author>
<author><name sortKey="Wang, Y" uniqKey="Wang Y">Y. Wang</name>
</author>
<author><name sortKey="Luo, C" uniqKey="Luo C">C. Luo</name>
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</author>
<author><name sortKey="Badruddin, N" uniqKey="Badruddin N">N. Badruddin</name>
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<author><name sortKey="Drieberg, M" uniqKey="Drieberg M">M. Drieberg</name>
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</author>
<author><name sortKey="Ahn, S" uniqKey="Ahn S">S. Ahn</name>
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