Convolutional Neural Network for Drowsiness Detection Using EEG Signals.
Identifieur interne : 000059 ( Main/Exploration ); précédent : 000058; suivant : 000060Convolutional Neural Network for Drowsiness Detection Using EEG Signals.
Auteurs : Siwar Chaabene [Tunisie] ; Bassem Bouaziz [Tunisie] ; Amal Boudaya [Tunisie] ; Anita Hökelmann [Allemagne] ; Achraf Ammar [Allemagne, France] ; Lotfi Chaari [France]Source :
- 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 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.
DOI: 10.3390/s21051734
PubMed: 33802357
PubMed Central: PMC7959292
Affiliations:
- Allemagne, France, Tunisie
- Midi-Pyrénées, Occitanie (région administrative), Saxe-Anhalt, Île-de-France
- Magdebourg, Nanterre, Toulouse
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type="city">Magdebourg</settlement></placeName></affiliation><affiliation wicri:level="3"><nlm:affiliation>Interdisciplinary Laboratory in Neurosciences, Physiology and Psychology: Physical Activity, Health and Learning (LINP2), UFR STAPS, UPL, Paris Nanterre University, 92000 Nanterre, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>Interdisciplinary Laboratory in Neurosciences, Physiology and Psychology: Physical Activity, Health and Learning (LINP2), UFR STAPS, UPL, Paris Nanterre University, 92000 Nanterre</wicri:regionArea><placeName><region type="region" nuts="2">Île-de-France</region><settlement type="city">Nanterre</settlement></placeName></affiliation></author><author><name sortKey="Chaari, Lotfi" sort="Chaari, Lotfi" uniqKey="Chaari L" first="Lotfi" last="Chaari">Lotfi Chaari</name><affiliation wicri:level="3"><nlm:affiliation>IRIT-ENSEEIHT, University of Toulouse, 31013 Toulouse, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>IRIT-ENSEEIHT, University of Toulouse, 31013 Toulouse</wicri:regionArea><placeName><region type="region" nuts="2">Occitanie (région administrative)</region><region type="old region" nuts="2">Midi-Pyrénées</region><settlement type="city">Toulouse</settlement></placeName></affiliation></author></analytic><series><title level="j">Sensors (Basel, Switzerland)</title><idno type="eISSN">1424-8220</idno><imprint><date when="2021" type="published">2021</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Electroencephalography (MeSH)</term><term>Neural Networks, Computer (MeSH)</term><term>Signal Processing, Computer-Assisted (MeSH)</term><term>Wakefulness (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Traitement du signal assisté par ordinateur (MeSH)</term><term>Vigilance (MeSH)</term><term>Électroencéphalographie (MeSH)</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Electroencephalography</term><term>Neural Networks, Computer</term><term>Signal Processing, Computer-Assisted</term><term>Wakefulness</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Traitement du signal assisté par ordinateur</term><term>Vigilance</term><term>Électroencéphalographie</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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 <i>Emotiv EPOC+</i> 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.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Automated" Owner="NLM"><PMID Version="1">33802357</PMID><DateCompleted><Year>2021</Year><Month>04</Month><Day>27</Day></DateCompleted><DateRevised><Year>2021</Year><Month>04</Month><Day>27</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1424-8220</ISSN><JournalIssue CitedMedium="Internet"><Volume>21</Volume><Issue>5</Issue><PubDate><Year>2021</Year><Month>Mar</Month><Day>03</Day></PubDate></JournalIssue><Title>Sensors (Basel, Switzerland)</Title><ISOAbbreviation>Sensors (Basel)</ISOAbbreviation></Journal><ArticleTitle>Convolutional Neural Network for Drowsiness Detection Using EEG Signals.</ArticleTitle><ELocationID EIdType="pii" ValidYN="Y">1734</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.3390/s21051734</ELocationID><Abstract><AbstractText>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 <i>Emotiv EPOC+</i> 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.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Chaabene</LastName><ForeName>Siwar</ForeName><Initials>S</Initials><Identifier Source="ORCID">0000-0002-6458-9569</Identifier><AffiliationInfo><Affiliation>Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACL), University of Sfax, Sfax 3021, Tunisia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Digital Research Center of Sfax, B.P. 275, Sakiet Ezzit, Sfax 3021, Tunisia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bouaziz</LastName><ForeName>Bassem</ForeName><Initials>B</Initials><Identifier Source="ORCID">0000-0002-3692-9482</Identifier><AffiliationInfo><Affiliation>Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACL), University of Sfax, Sfax 3021, Tunisia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Digital Research Center of Sfax, B.P. 275, Sakiet Ezzit, Sfax 3021, Tunisia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Boudaya</LastName><ForeName>Amal</ForeName><Initials>A</Initials><Identifier Source="ORCID">0000-0003-0486-7923</Identifier><AffiliationInfo><Affiliation>Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACL), University of Sfax, Sfax 3021, Tunisia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Digital Research Center of Sfax, B.P. 275, Sakiet Ezzit, Sfax 3021, Tunisia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hökelmann</LastName><ForeName>Anita</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Institute of Sport Science, Otto-von-Guericke University Magdeburg, 39104 Magdeburg, Germany.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ammar</LastName><ForeName>Achraf</ForeName><Initials>A</Initials><Identifier Source="ORCID">0000-0003-0347-8053</Identifier><AffiliationInfo><Affiliation>Institute of Sport Science, Otto-von-Guericke University Magdeburg, 39104 Magdeburg, Germany.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Interdisciplinary Laboratory in Neurosciences, Physiology and Psychology: Physical Activity, Health and Learning (LINP2), UFR STAPS, UPL, Paris Nanterre University, 92000 Nanterre, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chaari</LastName><ForeName>Lotfi</ForeName><Initials>L</Initials><Identifier Source="ORCID">0000-0002-3590-0370</Identifier><AffiliationInfo><Affiliation>IRIT-ENSEEIHT, University of Toulouse, 31013 Toulouse, France.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal 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