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

In 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 processe...

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Fecha de publicación:: 2017

Institución:: Universidad de Medellín

Repositorio:: Repositorio UDEM

Idioma:: eng

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network_acronym_str	REPOUDEM2
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repository_id_str
dc.title.spa.fl_str_mv	An approach to emotion recognition in single-channel EEG signals using stationarywavelet transform
title	An approach to emotion recognition in single-channel EEG signals using stationarywavelet transform
spellingShingle	An approach to emotion recognition in single-channel EEG signals using stationarywavelet transform EEG Emotion Features KNN QDA RFC Wavelet Biomedical engineering Electroencephalography Electrophysiology Signal processing Speech recognition Developmental psychology Emotion Emotion recognition Emotional state Features Single channel eeg Single-channel signals Wavelet Biomedical signal processing
title_short	An approach to emotion recognition in single-channel EEG signals using stationarywavelet transform
title_full	An approach to emotion recognition in single-channel EEG signals using stationarywavelet transform
title_fullStr	An approach to emotion recognition in single-channel EEG signals using stationarywavelet transform
title_full_unstemmed	An approach to emotion recognition in single-channel EEG signals using stationarywavelet transform
title_sort	An approach to emotion recognition in single-channel EEG signals using stationarywavelet transform
dc.contributor.affiliation.spa.fl_str_mv	Gómez, A., Mathematical Modeling Research Group, GRIMMAT, Universidad EAFIT, Medellín, Colombia Quintero, L., Mathematical Modeling Research Group, GRIMMAT, Universidad EAFIT, Medellín, Colombia López, N., Medical Technology Laboratory, GATEME, Universidad Nacional de San Juan, San Juan, Argentina Castro, J., Psychology, Education and Culture Research Group, Institución Universitaria Politécnico Grancolombiano, Bogotá, Colombia Villa, L., System Engineering Research Group, ARKADIUS, Universidad de Medellín, Medellín, Colombia Mejía, G., Functional Analysis and Aplications Research Group, Universidad EAFIT, Medellín, Colombia
dc.subject.keyword.eng.fl_str_mv	EEG Emotion Features KNN QDA RFC Wavelet Biomedical engineering Electroencephalography Electrophysiology Signal processing Speech recognition Developmental psychology Emotion Emotion recognition Emotional state Features Single channel eeg Single-channel signals Wavelet Biomedical signal processing
topic	EEG Emotion Features KNN QDA RFC Wavelet Biomedical engineering Electroencephalography Electrophysiology Signal processing Speech recognition Developmental psychology Emotion Emotion recognition Emotional state Features Single channel eeg Single-channel signals Wavelet Biomedical signal processing
description	In 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.
publishDate	2017
dc.date.accessioned.none.fl_str_mv	2017-12-19T19:36:50Z
dc.date.available.none.fl_str_mv	2017-12-19T19:36:50Z
dc.date.created.none.fl_str_mv	2017
dc.type.eng.fl_str_mv	Conference Paper
dc.type.coarversion.fl_str_mv	http://purl.org/coar/version/c_970fb48d4fbd8a85
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dc.type.driver.none.fl_str_mv	info:eu-repo/semantics/conferenceObject
dc.identifier.isbn.none.fl_str_mv	9789811040856
dc.identifier.issn.none.fl_str_mv	16800737
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/11407/4354
dc.identifier.doi.none.fl_str_mv	10.1007/978-981-10-4086-3_164
dc.identifier.reponame.spa.fl_str_mv	reponame:Repositorio Institucional Universidad de Medellín
dc.identifier.instname.spa.fl_str_mv	instname:Universidad de Medellín
identifier_str_mv	9789811040856 16800737 10.1007/978-981-10-4086-3_164 reponame:Repositorio Institucional Universidad de Medellín instname:Universidad de Medellín
url	http://hdl.handle.net/11407/4354
dc.language.iso.none.fl_str_mv	eng
language	eng
dc.relation.isversionof.spa.fl_str_mv	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018372191&doi=10.1007%2f978-981-10-4086-3_164&partnerID=40&md5=2ff10283d5e0d903cdc3699c25094a24
dc.relation.ispartofes.spa.fl_str_mv	IFMBE Proceedings
dc.relation.references.spa.fl_str_mv	Michel, C., Michel, C., What is emotion Behavioural Processes, 60, pp. 69-83 Scherer, K.R., What are emotions And How Can they Be Measured? Affrunti Nicholas, W., Janet, W.-B., The effect of maternal psychopathology on parentchild agreement of child anxiety symptoms: A hierarchical linear modeling approach Journal of Anxiety Disorders, 32, pp. 56-65 Kleiman Evan, M., Brooke, A., Look Amy, E., Berman Mitchell, E., McCloskey Michael, S., The role of emotion reactivity and gender in the relationship between psychopathology and self-injurious behavior Personality and Individual Differences, 69, pp. 150-155 Juan, M.-M., Arantza, A., Emotions in human and artificial intelligence Computers in Human Behavior, 21, pp. 323-341 Kreps Gary, L., Linda, N., Artificial intelligence and immediacy: Designing health communication to personally engage consumers and providers Patient Education and Counseling, 92, pp. 205-210 Milton, A., Selvi, S., Tamil. Class-Specific Multiple Classifiers Scheme to Recognize Emotions from Speech Signals, 28, pp. 727-742 Xiaoyan, F., Junzo, W., Building a Recognition System of Speech Emotion and Emotional States 2013 Second International Conference on Robot Vision and Signal Processing, pp. 253-258 Kenny, D.A., Kashy, D.A., Cook, W.L., Dyadic Data Analysis Methodology in the Social Sciencesguilford Press Killeen Lauren, A., Understanding Parenting as a Process: Frontal EEG Alpha Asymmetry as a Measure of”online” Maternal Responsiveness to Infant Cues Alberto, C.M.J., Neurodinámica Y autoorganización En La interacción Socioemocional Madre-Hijo: aproximación De Los Sistemas dinámicos a Los Principios Del Desarrollo Emocional Infantil, 2, p. 17 Alberto, C.M.J., Sistemas dinámicos En La interacción Emocional Madre-Hijo: Primera Fase, 9, pp. 129-138 Saeid, S., Jonathon, C., EEG Signal Processing, p. 1 Michal, T., Fundamentals of EEG Measurement Measurement Science Review, 2, pp. 1-11 Petrantonakis Panagiotis, C., Hadjileontiadis Leontios, J., Emotion recognition from EEG using higher order crossings IEEE Transactions on Information Technology in Biomedicine: A Publication of the IEEE Engineering in Medicine and Biology Society, 14, pp. 186-197 Yun, L.Y., Shulan, H., Classifying different emotional states by means of eegbased functional connectivity patterns Plos ONE, p. 9 Pachori, V.B.R.B., Classification of Human Emotions Based on Multiwavelet Transform of EEG Signals Murugappan, M., Classification of Human Emotion from EEG Using Discrete Wavelet Transform Alejandro, G., Lucia, Q., Natalia, L., Jaime, C., An approach to emotion recognition in single-channel EEG signals: A mother child interaction XX Congreso Argentino De Bioingeniera, SABI 2015 Nason, G.P., Silverman, B.W., The Stationary Wavelet Transform and Some Statistical Applications, pp. 281-300. , Springer-Verlag Campo, D., Quintero, O.L., Bastidas, M., Multiresolution analysis (Discrete wavelet transform) through Daubechies family for emotion recognition in speech Journal of Physics: Conference Series, 705, p. 12034 Bustamante, P.A., Lopez, C.N.M., Perez, M.E., Quintero, M.O.L., Recognition and regionalization of emotions in the arousalvalence plane Conf Proc IEEE Eng Med Biol Soc, pp. 6042-6045 Kevin, P., Macgillivray, B.H.L., Kurtosis: A Critical Review The American Statistician, 42, pp. 111-119 Geoff, D., Pattern Recognition and Classification, 53 Gareth, J., Daniela, W., Trevor, H., Robert, T., An Introduction to Statistical Learning, p. 103
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_16ec
rights_invalid_str_mv	http://purl.org/coar/access_right/c_16ec
dc.publisher.spa.fl_str_mv	Springer Verlag
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingenierías
dc.source.spa.fl_str_mv	Scopus
institution	Universidad de Medellín
repository.name.fl_str_mv	Repositorio Institucional Universidad de Medellin
repository.mail.fl_str_mv	repositorio@udem.edu.co
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spelling	2017-12-19T19:36:50Z2017-12-19T19:36:50Z2017978981104085616800737http://hdl.handle.net/11407/435410.1007/978-981-10-4086-3_164reponame:Repositorio Institucional Universidad de Medellíninstname:Universidad de MedellínIn 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.engSpringer VerlagFacultad de Ingenieríashttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85018372191&doi=10.1007%2f978-981-10-4086-3_164&partnerID=40&md5=2ff10283d5e0d903cdc3699c25094a24IFMBE ProceedingsMichel, C., Michel, C., What is emotion Behavioural Processes, 60, pp. 69-83Scherer, K.R., What are emotions And How Can they Be Measured?Affrunti Nicholas, W., Janet, W.-B., The effect of maternal psychopathology on parentchild agreement of child anxiety symptoms: A hierarchical linear modeling approach Journal of Anxiety Disorders, 32, pp. 56-65Kleiman Evan, M., Brooke, A., Look Amy, E., Berman Mitchell, E., McCloskey Michael, S., The role of emotion reactivity and gender in the relationship between psychopathology and self-injurious behavior Personality and Individual Differences, 69, pp. 150-155Juan, M.-M., Arantza, A., Emotions in human and artificial intelligence Computers in Human Behavior, 21, pp. 323-341Kreps Gary, L., Linda, N., Artificial intelligence and immediacy: Designing health communication to personally engage consumers and providers Patient Education and Counseling, 92, pp. 205-210Milton, A., Selvi, S., Tamil. Class-Specific Multiple Classifiers Scheme to Recognize Emotions from Speech Signals, 28, pp. 727-742Xiaoyan, F., Junzo, W., Building a Recognition System of Speech Emotion and Emotional States 2013 Second International Conference on Robot Vision and Signal Processing, pp. 253-258Kenny, D.A., Kashy, D.A., Cook, W.L., Dyadic Data Analysis Methodology in the Social Sciencesguilford PressKilleen Lauren, A., Understanding Parenting as a Process: Frontal EEG Alpha Asymmetry as a Measure of”online” Maternal Responsiveness to Infant CuesAlberto, C.M.J., Neurodinámica Y autoorganización En La interacción Socioemocional Madre-Hijo: aproximación De Los Sistemas dinámicos a Los Principios Del Desarrollo Emocional Infantil, 2, p. 17Alberto, C.M.J., Sistemas dinámicos En La interacción Emocional Madre-Hijo: Primera Fase, 9, pp. 129-138Saeid, S., Jonathon, C., EEG Signal Processing, p. 1Michal, T., Fundamentals of EEG Measurement Measurement Science Review, 2, pp. 1-11Petrantonakis Panagiotis, C., Hadjileontiadis Leontios, J., Emotion recognition from EEG using higher order crossings IEEE Transactions on Information Technology in Biomedicine: A Publication of the IEEE Engineering in Medicine and Biology Society, 14, pp. 186-197Yun, L.Y., Shulan, H., Classifying different emotional states by means of eegbased functional connectivity patterns Plos ONE, p. 9Pachori, V.B.R.B., Classification of Human Emotions Based on Multiwavelet Transform of EEG SignalsMurugappan, M., Classification of Human Emotion from EEG Using Discrete Wavelet TransformAlejandro, G., Lucia, Q., Natalia, L., Jaime, C., An approach to emotion recognition in single-channel EEG signals: A mother child interaction XX Congreso Argentino De Bioingeniera, SABI 2015Nason, G.P., Silverman, B.W., The Stationary Wavelet Transform and Some Statistical Applications, pp. 281-300. , Springer-VerlagCampo, D., Quintero, O.L., Bastidas, M., Multiresolution analysis (Discrete wavelet transform) through Daubechies family for emotion recognition in speech Journal of Physics: Conference Series, 705, p. 12034Bustamante, P.A., Lopez, C.N.M., Perez, M.E., Quintero, M.O.L., Recognition and regionalization of emotions in the arousalvalence plane Conf Proc IEEE Eng Med Biol Soc, pp. 6042-6045Kevin, P., Macgillivray, B.H.L., Kurtosis: A Critical Review The American Statistician, 42, pp. 111-119Geoff, D., Pattern Recognition and Classification, 53Gareth, J., Daniela, W., Trevor, H., Robert, T., An Introduction to Statistical Learning, p. 103ScopusAn approach to emotion recognition in single-channel EEG signals using stationarywavelet transformConference Paperinfo:eu-repo/semantics/conferenceObjecthttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fGómez, A., Mathematical Modeling Research Group, GRIMMAT, Universidad EAFIT, Medellín, ColombiaQuintero, L., Mathematical Modeling Research Group, GRIMMAT, Universidad EAFIT, Medellín, ColombiaLópez, N., Medical Technology Laboratory, GATEME, Universidad Nacional de San Juan, San Juan, ArgentinaCastro, J., Psychology, Education and Culture Research Group, Institución Universitaria Politécnico Grancolombiano, Bogotá, ColombiaVilla, L., System Engineering Research Group, ARKADIUS, Universidad de Medellín, Medellín, ColombiaMejía, G., Functional Analysis and Aplications Research Group, Universidad EAFIT, Medellín, ColombiaGómez A.Quintero L.López N.Castro J.Villa L.Mejía G.Mathematical Modeling Research Group, GRIMMAT, Universidad EAFIT, Medellín, ColombiaMedical Technology Laboratory, GATEME, Universidad Nacional de San Juan, San Juan, ArgentinaPsychology, Education and Culture Research Group, Institución Universitaria Politécnico Grancolombiano, Bogotá, ColombiaSystem Engineering Research Group, ARKADIUS, Universidad de Medellín, Medellín, ColombiaFunctional Analysis and Aplications Research Group, Universidad EAFIT, Medellín, ColombiaEEGEmotionFeaturesKNNQDARFCWaveletBiomedical engineeringElectroencephalographyElectrophysiologySignal processingSpeech recognitionDevelopmental psychologyEmotionEmotion recognitionEmotional stateFeaturesSingle channel eegSingle-channel signalsWaveletBiomedical signal processingIn 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.http://purl.org/coar/access_right/c_16ec11407/4354oai:repository.udem.edu.co:11407/43542020-05-27 17:34:51.266Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co

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

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