A novel approach to emotion recognition using local subset feature selection and modified Dempster-Shafer theory.
Identifieur interne : 000895 ( Main/Exploration ); précédent : 000894; suivant : 000896A novel approach to emotion recognition using local subset feature selection and modified Dempster-Shafer theory.
Auteurs : Morteza Zangeneh Soroush [Iran] ; Keivan Maghooli [Iran] ; Seyed Kamaledin Setarehdan [Iran] ; Ali Motie Nasrabadi [Iran]Source :
- Behavioral and brain functions : BBF [ 1744-9081 ] ; 2018.
Descripteurs français
- KwdFr :
- MESH :
- méthodes : Enregistrement sur magnétoscope, Électroencéphalographie.
- physiologie : Encéphale, Émotions.
- psychologie : Musique.
- Adulte, Apprentissage machine, Bases de données factuelles, Femelle, Humains, Jeune adulte, Mâle.
English descriptors
- KwdEn :
- MESH :
- methods : Electroencephalography, Video Recording.
- physiology : Brain, Emotions, Recognition, Psychology.
- psychology : Music.
- Adult, Databases, Factual, Female, Humans, Machine Learning, Male, Young Adult.
Abstract
BACKGROUND
Emotion recognition is an increasingly important field of research in brain computer interactions.
INTRODUCTION
With the advance of technology, automatic emotion recognition systems no longer seem far-fetched. Be that as it may, detecting neural correlates of emotion has remained a substantial bottleneck. Settling this issue will be a breakthrough of significance in the literature.
METHODS
The current study aims to identify the correlations between different emotions and brain regions with the help of suitable electrodes. Initially, independent component analysis algorithm is employed to remove artifacts and extract the independent components. The informative channels are then selected based on the thresholded average activity value for obtained components. Afterwards, effective features are extracted from selected channels common between all emotion classes. Features are reduced using the local subset feature selection method and then fed to a new classification model using modified Dempster-Shafer theory of evidence.
RESULTS
The presented method is employed to DEAP dataset and the results are compared to those of previous studies, which highlights the significant ability of this method to recognize emotions through electroencephalography, by the accuracy of about 91%. Finally, the obtained results are discussed and new aspects are introduced.
CONCLUSIONS
The present study addresses the long-standing challenge of finding neural correlates between human emotions and the activated brain regions. Also, we managed to solve uncertainty problem in emotion classification which is one of the most challenging issues in this field. The proposed method could be employed in other practical applications in future.
DOI: 10.1186/s12993-018-0149-4
PubMed: 30382882
PubMed Central: PMC6208176
Affiliations:
Links toward previous steps (curation, corpus...)
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Tehran</wicri:regionArea><wicri:noRegion>Tehran</wicri:noRegion></affiliation></author><author><name sortKey="Maghooli, Keivan" sort="Maghooli, Keivan" uniqKey="Maghooli K" first="Keivan" last="Maghooli">Keivan Maghooli</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. k_maghooli@srbiau.ac.ir.</nlm:affiliation><country xml:lang="fr">Iran</country><wicri:regionArea>Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran</wicri:regionArea><wicri:noRegion>Tehran</wicri:noRegion></affiliation></author><author><name sortKey="Setarehdan, Seyed Kamaledin" sort="Setarehdan, Seyed Kamaledin" uniqKey="Setarehdan S" first="Seyed Kamaledin" last="Setarehdan">Seyed Kamaledin Setarehdan</name><affiliation wicri:level="1"><nlm:affiliation>Control and Intelligent Processing Centre of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.</nlm:affiliation><country xml:lang="fr">Iran</country><wicri:regionArea>Control and Intelligent Processing Centre of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran</wicri:regionArea><wicri:noRegion>Tehran</wicri:noRegion></affiliation></author><author><name sortKey="Nasrabadi, Ali Motie" sort="Nasrabadi, Ali Motie" uniqKey="Nasrabadi A" first="Ali Motie" last="Nasrabadi">Ali Motie Nasrabadi</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.</nlm:affiliation><country xml:lang="fr">Iran</country><wicri:regionArea>Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran</wicri:regionArea><wicri:noRegion>Tehran</wicri:noRegion></affiliation></author></analytic><series><title level="j">Behavioral and brain functions : BBF</title><idno type="eISSN">1744-9081</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult (MeSH)</term><term>Brain (physiology)</term><term>Databases, Factual (MeSH)</term><term>Electroencephalography (methods)</term><term>Emotions (physiology)</term><term>Female (MeSH)</term><term>Humans (MeSH)</term><term>Machine Learning (MeSH)</term><term>Male (MeSH)</term><term>Music (psychology)</term><term>Recognition, Psychology (physiology)</term><term>Video Recording (methods)</term><term>Young Adult (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adulte (MeSH)</term><term>Apprentissage machine (MeSH)</term><term>Bases de données factuelles (MeSH)</term><term>Encéphale (physiologie)</term><term>Enregistrement sur magnétoscope (méthodes)</term><term>Femelle (MeSH)</term><term>Humains (MeSH)</term><term>Jeune adulte (MeSH)</term><term>Musique (psychologie)</term><term>Mâle (MeSH)</term><term>Électroencéphalographie (méthodes)</term><term>Émotions (physiologie)</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Electroencephalography</term><term>Video Recording</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Enregistrement sur magnétoscope</term><term>Électroencéphalographie</term></keywords><keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Encéphale</term><term>Émotions</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Brain</term><term>Emotions</term><term>Recognition, Psychology</term></keywords><keywords scheme="MESH" qualifier="psychologie" xml:lang="fr"><term>Musique</term></keywords><keywords scheme="MESH" qualifier="psychology" xml:lang="en"><term>Music</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adult</term><term>Databases, Factual</term><term>Female</term><term>Humans</term><term>Machine Learning</term><term>Male</term><term>Young Adult</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adulte</term><term>Apprentissage machine</term><term>Bases de données factuelles</term><term>Femelle</term><term>Humains</term><term>Jeune adulte</term><term>Mâle</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>Emotion recognition is an increasingly important field of research in brain computer interactions.</p></div><div type="abstract" xml:lang="en"><p><b>INTRODUCTION</b></p><p>With the advance of technology, automatic emotion recognition systems no longer seem far-fetched. Be that as it may, detecting neural correlates of emotion has remained a substantial bottleneck. Settling this issue will be a breakthrough of significance in the literature.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>The current study aims to identify the correlations between different emotions and brain regions with the help of suitable electrodes. Initially, independent component analysis algorithm is employed to remove artifacts and extract the independent components. The informative channels are then selected based on the thresholded average activity value for obtained components. Afterwards, effective features are extracted from selected channels common between all emotion classes. Features are reduced using the local subset feature selection method and then fed to a new classification model using modified Dempster-Shafer theory of evidence.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>The presented method is employed to DEAP dataset and the results are compared to those of previous studies, which highlights the significant ability of this method to recognize emotions through electroencephalography, by the accuracy of about 91%. Finally, the obtained results are discussed and new aspects are introduced.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>The present study addresses the long-standing challenge of finding neural correlates between human emotions and the activated brain regions. Also, we managed to solve uncertainty problem in emotion classification which is one of the most challenging issues in this field. The proposed method could be employed in other practical applications in future.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">30382882</PMID><DateCompleted><Year>2019</Year><Month>04</Month><Day>04</Day></DateCompleted><DateRevised><Year>2019</Year><Month>12</Month><Day>10</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1744-9081</ISSN><JournalIssue CitedMedium="Internet"><Volume>14</Volume><Issue>1</Issue><PubDate><Year>2018</Year><Month>Oct</Month><Day>31</Day></PubDate></JournalIssue><Title>Behavioral and brain functions : BBF</Title><ISOAbbreviation>Behav Brain Funct</ISOAbbreviation></Journal><ArticleTitle>A novel approach to emotion recognition using local subset feature selection and modified Dempster-Shafer theory.</ArticleTitle><Pagination><MedlinePgn>17</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12993-018-0149-4</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Emotion recognition is an increasingly important field of research in brain computer interactions.</AbstractText><AbstractText Label="INTRODUCTION" NlmCategory="BACKGROUND">With the advance of technology, automatic emotion recognition systems no longer seem far-fetched. Be that as it may, detecting neural correlates of emotion has remained a substantial bottleneck. Settling this issue will be a breakthrough of significance in the literature.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">The current study aims to identify the correlations between different emotions and brain regions with the help of suitable electrodes. Initially, independent component analysis algorithm is employed to remove artifacts and extract the independent components. The informative channels are then selected based on the thresholded average activity value for obtained components. Afterwards, effective features are extracted from selected channels common between all emotion classes. Features are reduced using the local subset feature selection method and then fed to a new classification model using modified Dempster-Shafer theory of evidence.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">The presented method is employed to DEAP dataset and the results are compared to those of previous studies, which highlights the significant ability of this method to recognize emotions through electroencephalography, by the accuracy of about 91%. Finally, the obtained results are discussed and new aspects are introduced.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">The present study addresses the long-standing challenge of finding neural correlates between human emotions and the activated brain regions. Also, we managed to solve uncertainty problem in emotion classification which is one of the most challenging issues in this field. The proposed method could be employed in other practical applications in future.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Zangeneh Soroush</LastName><ForeName>Morteza</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Maghooli</LastName><ForeName>Keivan</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. k_maghooli@srbiau.ac.ir.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Setarehdan</LastName><ForeName>Seyed Kamaledin</ForeName><Initials>SK</Initials><AffiliationInfo><Affiliation>Control and Intelligent Processing Centre of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Nasrabadi</LastName><ForeName>Ali Motie</ForeName><Initials>AM</Initials><AffiliationInfo><Affiliation>Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2018</Year><Month>10</Month><Day>31</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Behav Brain Funct</MedlineTA><NlmUniqueID>101245751</NlmUniqueID><ISSNLinking>1744-9081</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001921" MajorTopicYN="N">Brain</DescriptorName><QualifierName 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MajorTopicYN="N">Music</DescriptorName><QualifierName UI="Q000523" MajorTopicYN="N">psychology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D021641" MajorTopicYN="N">Recognition, Psychology</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D014741" MajorTopicYN="N">Video Recording</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D055815" MajorTopicYN="N">Young Adult</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Brain computer interactions</Keyword><Keyword MajorTopicYN="N">Dempster Shafer theory</Keyword><Keyword MajorTopicYN="N">Emotion identification</Keyword><Keyword MajorTopicYN="N">Independent component analysis</Keyword><Keyword MajorTopicYN="N">Local subset feature selection</Keyword><Keyword MajorTopicYN="N">Machine learning 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first="Morteza" last="Zangeneh Soroush">Morteza Zangeneh Soroush</name></noRegion><name sortKey="Maghooli, Keivan" sort="Maghooli, Keivan" uniqKey="Maghooli K" first="Keivan" last="Maghooli">Keivan Maghooli</name><name sortKey="Nasrabadi, Ali Motie" sort="Nasrabadi, Ali Motie" uniqKey="Nasrabadi A" first="Ali Motie" last="Nasrabadi">Ali Motie Nasrabadi</name><name sortKey="Setarehdan, Seyed Kamaledin" sort="Setarehdan, Seyed Kamaledin" uniqKey="Setarehdan S" first="Seyed Kamaledin" last="Setarehdan">Seyed Kamaledin Setarehdan</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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