An approach to emotion recognition in single-channel EEG signals: a mother child interaction

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dc.citation.epage8
dc.citation.issue1
dc.citation.journalTitleJournal of Physics: Conference Serieseng
dc.citation.spage1
dc.citation.volume705
dc.contributor.affiliationMathematical Modeling Research Group, GRIMMAT, School of Sciences, Universidad EAFIT, Medellín, Colombiaspa
dc.contributor.affiliationMedical Technology Laboratory, Faculty of Engineering, Universidad Nacional de San Juan, Argentinaspa
dc.contributor.affiliationPsychology, Education and Culture Research Group Faculty of Social Science Politécnico Grancolombiano University Institution, Argentinaspa
dc.contributor.authorGómez, A.spa
dc.contributor.authorQuintero, L.spa
dc.contributor.authorLópez, N.spa
dc.contributor.authorCastro, J.spa
dc.contributor.authorGómez, A.
dc.contributor.authorQuintero, L.
dc.contributor.authorLópez, N.
dc.contributor.authorCastro, J.
dc.contributor.departmentUniversidad EAFIT. Escuela de Cienciasspa
dc.contributor.eafitauthoragomez13@eafit.edu.co
dc.contributor.eafitauthoroquinte1@eafit.edu.co
dc.contributor.researchgroupModelado Matemáticospa
dc.date2016
dc.date.accessioned2016-05-11T20:44:07Z
dc.date.available2016-05-11T20:44:07Z
dc.date.issued2016
dc.description.abstractIn this work, we perform a first approach to emotion recognition from EEG single channel signals extracted in four (4) mother-child dyads experiment in developmental psychology -- Single channel EEG signals are analyzed and processed using several window sizes by performing a statistical analysis over features in the time and frequency domains -- Finally, a neural network obtained an average accuracy rate of 99% of classification in two emotional states such as happiness and sadnesseng
dc.description.note20th Argentinean Bioengineering Society Congress, SABI 2015 (XX Congreso Argentino de Bioingeniería y IX Jornadas de Ingeniería Clínica)28–30 October 2015, San Nicolás de los Arroyos, Argentinaeng
dc.formatapplication/pdf
dc.identifier.doi10.1088/1742-6596/705/1/012051
dc.identifier.issn1742-6596
dc.identifier.urihttp://hdl.handle.net/10784/8372
dc.language.isoengeng
dc.publisherIOP Publishing
dc.relation.ispartofJournal of Physics: Conference Series; Vol. 705, Núm. 1 (2016); pp.8spa
dc.relation.isversionofhttp://dx.doi.org/10.1088/1742-6596/705/1/012051
dc.relation.urihttp://dx.doi.org/10.1088/1742-6596/705/1/012051
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesseng
dc.rights.licenseCreative Commons Attribution 3.0 licence (CC BY 3.0)eng
dc.rights.localAcceso abiertospa
dc.sourceJournal of Physics: Conference Series
dc.subjectSistemas dinámicos
dc.subjectAtractores (Matemáticas)
dc.subject.keywordElectroencephalography
dc.subject.keywordNeural networks (Computer science)
dc.subject.keywordEmotions
dc.subject.keywordBranching processes
dc.subject.keywordStochastic processes
dc.subject.keywordNonverbal communication
dc.subject.keywordExpression
dc.subject.keywordSignal processing
dc.subject.keywordMother and child
dc.subject.keywordRecognition systems
dc.subject.keywordElectroencephalographyeng
dc.subject.keywordNeural networks (Computer science)eng
dc.subject.keywordEmotionseng
dc.subject.keywordBranching processeseng
dc.subject.keywordStochastic processeseng
dc.subject.keywordNonverbal communicationeng
dc.subject.keywordExpressioneng
dc.subject.keywordSignal processingeng
dc.subject.keywordMother and childeng
dc.subject.keywordRecognition systemseng
dc.subject.keywordSistemas dinámicosspa
dc.subject.keywordAtractores (Matemáticas)spa
dc.subject.lembELECTROENCEFALOGRAFÍA
dc.subject.lembREDES NEURALES (COMPUTADORES)
dc.subject.lembEMOCIONES
dc.subject.lembPROCESOS DE BIFURCACIÓN
dc.subject.lembPROCESOS ESTOCÁSTICOS
dc.subject.lembCOMUNICACIÓN NO VERBAL
dc.subject.lembEXPRESIÓN
dc.subject.lembPROCESAMIENTO DE SEÑALES
dc.subject.lembMADRE E HIJO
dc.subject.lembSISTEMAS DE RECONOCIMIENTO
dc.titleAn approach to emotion recognition in single-channel EEG signals: a mother child interactioneng
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typearticle
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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