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An approach to emotion recognition in single-channel EEG signals using stationarywavelet transform

dc.creatorGómez A.spa
dc.creatorQuintero L.spa
dc.creatorLópez N.spa
dc.creatorCastro J.spa
dc.creatorVilla L.spa
dc.creatorMejía G.spa
dc.date.accessioned2017-12-19T19:36:50Z
dc.date.available2017-12-19T19:36:50Z
dc.date.created2017
dc.identifier.isbn9789811040856
dc.identifier.issn16800737
dc.identifier.urihttp://hdl.handle.net/11407/4354
dc.description.abstractIn this work, we perform an approach to emotion recognition from Electroencephalography (EEG) single channel signals extracted in four (4) mother-child dyads experiment in developmental psychology. Single channel EEG signals are decomposed by several types of wavelets and each subsignal are processed using several window sizes by performing a statistical analysis. Finally, three types of classifiers were used, obtaining accuracy rate between 50% to 87% for the emotional states such as happiness, sadness and neutrality. © Springer Nature Singapore Pte Ltd. 2017.eng
dc.language.isoeng
dc.publisherSpringer Verlagspa
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85018372191&doi=10.1007%2f978-981-10-4086-3_164&partnerID=40&md5=2ff10283d5e0d903cdc3699c25094a24spa
dc.sourceScopusspa
dc.titleAn approach to emotion recognition in single-channel EEG signals using stationarywavelet transformspa
dc.typeConference Papereng
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.contributor.affiliationGómez, A., Mathematical Modeling Research Group, GRIMMAT, Universidad EAFIT, Medellín, Colombiaspa
dc.contributor.affiliationQuintero, L., Mathematical Modeling Research Group, GRIMMAT, Universidad EAFIT, Medellín, Colombiaspa
dc.contributor.affiliationLópez, N., Medical Technology Laboratory, GATEME, Universidad Nacional de San Juan, San Juan, Argentinaspa
dc.contributor.affiliationCastro, J., Psychology, Education and Culture Research Group, Institución Universitaria Politécnico Grancolombiano, Bogotá, Colombiaspa
dc.contributor.affiliationVilla, L., System Engineering Research Group, ARKADIUS, Universidad de Medellín, Medellín, Colombiaspa
dc.contributor.affiliationMejía, G., Functional Analysis and Aplications Research Group, Universidad EAFIT, Medellín, Colombiaspa
dc.identifier.doi10.1007/978-981-10-4086-3_164
dc.subject.keywordEEGeng
dc.subject.keywordEmotioneng
dc.subject.keywordFeatureseng
dc.subject.keywordKNNeng
dc.subject.keywordQDAeng
dc.subject.keywordRFCeng
dc.subject.keywordWaveleteng
dc.subject.keywordBiomedical engineeringeng
dc.subject.keywordElectroencephalographyeng
dc.subject.keywordElectrophysiologyeng
dc.subject.keywordSignal processingeng
dc.subject.keywordSpeech recognitioneng
dc.subject.keywordDevelopmental psychologyeng
dc.subject.keywordEmotioneng
dc.subject.keywordEmotion recognitioneng
dc.subject.keywordEmotional stateeng
dc.subject.keywordFeatureseng
dc.subject.keywordSingle channel eegeng
dc.subject.keywordSingle-channel signalseng
dc.subject.keywordWaveleteng
dc.subject.keywordBiomedical signal processingeng
dc.publisher.facultyFacultad de Ingenieríasspa
dc.abstractIn this work, we perform an approach to emotion recognition from Electroencephalography (EEG) single channel signals extracted in four (4) mother-child dyads experiment in developmental psychology. Single channel EEG signals are decomposed by several types of wavelets and each subsignal are processed using several window sizes by performing a statistical analysis. Finally, three types of classifiers were used, obtaining accuracy rate between 50% to 87% for the emotional states such as happiness, sadness and neutrality. © Springer Nature Singapore Pte Ltd. 2017.eng
dc.creator.affiliationMathematical Modeling Research Group, GRIMMAT, Universidad EAFIT, Medellín, Colombiaspa
dc.creator.affiliationMedical Technology Laboratory, GATEME, Universidad Nacional de San Juan, San Juan, Argentinaspa
dc.creator.affiliationPsychology, Education and Culture Research Group, Institución Universitaria Politécnico Grancolombiano, Bogotá, Colombiaspa
dc.creator.affiliationSystem Engineering Research Group, ARKADIUS, Universidad de Medellín, Medellín, Colombiaspa
dc.creator.affiliationFunctional Analysis and Aplications Research Group, Universidad EAFIT, Medellín, Colombiaspa
dc.relation.ispartofesIFMBE Proceedingsspa
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dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.driverinfo:eu-repo/semantics/conferenceObject
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellínspa
dc.identifier.instnameinstname:Universidad de Medellínspa


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