Characterization framework for Ex-combatants based on EEG and behavioral features

This paper presents a framework to characterize the emotional processing of Colombian ex-combatants from illegal groups. The classification process is performed using EEGERP data and behavioral features from psychological tests. The results show that ex-combatant and civilian populations can be auto...

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Tipo de recurso:
Fecha de publicación:
2017
Institución:
Universidad de Medellín
Repositorio:
Repositorio UDEM
Idioma:
eng
OAI Identifier:
oai:repository.udem.edu.co:11407/4351
Acceso en línea:
http://hdl.handle.net/11407/4351
Palabra clave:
Emotional processing
Emotional recognition task
ERP
Ex-combatants
Supervised learning
Artificial intelligence
Biomedical engineering
Decision support systems
Enterprise resource planning
Supervised learning
Testing
Behavioral features
Civilian populations
Classification process
Colombians
Emotional recognition
Ex-combatants
Psychological tests
Behavioral research
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http://purl.org/coar/access_right/c_16ec
id REPOUDEM2_256fe8efedcfbcae3d49d224d1c62faf
oai_identifier_str oai:repository.udem.edu.co:11407/4351
network_acronym_str REPOUDEM2
network_name_str Repositorio UDEM
repository_id_str
dc.title.spa.fl_str_mv Characterization framework for Ex-combatants based on EEG and behavioral features
title Characterization framework for Ex-combatants based on EEG and behavioral features
spellingShingle Characterization framework for Ex-combatants based on EEG and behavioral features
Emotional processing
Emotional recognition task
ERP
Ex-combatants
Supervised learning
Artificial intelligence
Biomedical engineering
Decision support systems
Enterprise resource planning
Supervised learning
Testing
Behavioral features
Civilian populations
Classification process
Colombians
Emotional recognition
Ex-combatants
Psychological tests
Behavioral research
title_short Characterization framework for Ex-combatants based on EEG and behavioral features
title_full Characterization framework for Ex-combatants based on EEG and behavioral features
title_fullStr Characterization framework for Ex-combatants based on EEG and behavioral features
title_full_unstemmed Characterization framework for Ex-combatants based on EEG and behavioral features
title_sort Characterization framework for Ex-combatants based on EEG and behavioral features
dc.contributor.affiliation.spa.fl_str_mv Quintero-Zea, A., SISTEMIC, Engineering Faculty, Universidad de Medell´ın UDEM, Cra. 87 No. 30 - 65, Medellín, Colombia
Sepúlveda-Cano, L.M., ARKADIUS, Engineering Faculty, Universidad de Medell´ın UDEM, Cra. 87 No. 30 - 65, Medellín, Colombia
Calvache, M.R., SISTEMIC, Engineering Faculty, Universidad de Medell´ın UDEM, Cra. 87 No. 30 - 65, Medellín, Colombia
Orrego, S.T., Mental Health Group, School of Public Health, Universidad de Antioquia UDEA, Cra. 53 No. 61-30, Medellín, Colombia
Orrego, N.T., Mental Health Group, School of Public Health, Universidad de Antioquia UDEA, Cra. 53 No. 61-30, Medellín, Colombia, Neuroscience Group, Faculty of Medicine, Universidad de Antioquia UDEA, Medellín, Colombia
dc.subject.keyword.eng.fl_str_mv Emotional processing
Emotional recognition task
ERP
Ex-combatants
Supervised learning
Artificial intelligence
Biomedical engineering
Decision support systems
Enterprise resource planning
Supervised learning
Testing
Behavioral features
Civilian populations
Classification process
Colombians
Emotional recognition
Ex-combatants
Psychological tests
Behavioral research
topic Emotional processing
Emotional recognition task
ERP
Ex-combatants
Supervised learning
Artificial intelligence
Biomedical engineering
Decision support systems
Enterprise resource planning
Supervised learning
Testing
Behavioral features
Civilian populations
Classification process
Colombians
Emotional recognition
Ex-combatants
Psychological tests
Behavioral research
description This paper presents a framework to characterize the emotional processing of Colombian ex-combatants from illegal groups. The classification process is performed using EEGERP data and behavioral features from psychological tests. The results show that ex-combatant and civilian populations can be automatically separated using supervised techniques.With this, we can provide a decision support system for psychologists to improve current interventions aimed to help ex-combatants to make a successful reintegration to civilian life. © 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
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_c94f
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/11407/4351
dc.identifier.doi.none.fl_str_mv 10.1007/978-981-10-4086-3_52
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_52
reponame:Repositorio Institucional Universidad de Medellín
instname:Universidad de Medellín
url http://hdl.handle.net/11407/4351
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-s2.0-85018389268&doi=10.1007%2f978-981-10-4086-3_52&partnerID=40&md5=bfebea4c0f97c7e3bb2d860a0035a255
dc.relation.ispartofes.spa.fl_str_mv IFMBE Proceedings
dc.relation.references.spa.fl_str_mv Carlos, T., Agustín, I., Lina, V., Emotional Processing in Colombian Ex-Combatants and Its Relationship with Empathy and Executive Functions Social Neuroscience, 10, pp. 153-165. , PMID: 25302548
I-Wei, S., Onton Julie, A., Nitin, P., O’Connell Ryan, M., Simmons Alan, N., Matthews Scott, C., Combat Veterans with PTSD after Mild TBI Exhibit Greater Erps from Posterior-Medial Cortical Areas while Appraising Facial Features, 155, pp. 234-240
Light Gregory, A., Williams Lisa, E., Falk, M., Electroencephalography (EEG) and Event-Related Potentials (Erps) with Human Participants Current Protocols in Neuroscience, p. 2524. , CHAPTER:Unit-6
Shravani, S., Sinha, V.K., Event-Related Potential: An Overview, 18, p. 70
Vilfredo, P.D., Enrica, S., Patrizia, R., Fabiola, V., Personality, Event-Related Potential (ERP) and Heart Rate (HR) in Emotional Word Processing Personality and Individual Differences, 36, pp. 873-891
Agustín, I., Hugo, U., Agustín, P., Neural Processing of Emotional Facial and Semantic Expressions in Euthymic Bipolar Disorder (BD) and Its Association with Theory of Mind (Tom) Plos ONE, 7, pp. 1-12
Arnaud, D., Scott, M., EEGLAB: An Open Source Toolbox for Analysis of Single-Trial EEG Dynamics including Independent Component Analysis Journal of Neuroscience Methods, 134, pp. 9-21
Gismero, E., EHS Escala De Habilidades Sociales Madrid: TEA Publicaciones De Psicología Aplicada
Adrian, R., Kenneth, D., Rolf, L., The Reactive-Proactive Aggression Questionnaire: Differential Correlates of Reactive and Proactive Aggression in Adolescent Boysaggressive Behavior, 32, pp. 159-171
Key Alexandra, P., Fonaryova, D.G.O., Maguire Mandy, J., Linking brainwaves to the brain An ERP Primer Developmental Neuropsychology, 27, pp. 183-215
Pawel, B., Semi-Supervised Feature Extraction Method Using Partial Least Squares and Gaussian Mixture Model Lecture Notes in Engineering and Computer Science
Herv, A., Partial least squares regression and projection on latent structure regression (PLS Regression) Wiley Interdisciplinary Reviews Computational Statistics, 2, pp. 97-106
Padraig, C., Jane, D.S., K-Nearest Neighbour Classifiers Multiple Classifier Systems, pp. 1-17
Vapnik Vladimir, N., The Nature of Statistical Learning Theorych Methods of Pattern Recognition, pp. 138-155. , New York, NY: Springer-Verlag2nd ed
Guo-You, S., Shuang, L., Model Selection of RBF Kernel for C-SVM Based on Genetic Algorithm and Multithreading in 2012 International Conference on Machine Learning and Cybernetics, 1, pp. 382-386
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
_version_ 1814159152479469568
spelling 2017-12-19T19:36:50Z2017-12-19T19:36:50Z2017978981104085616800737http://hdl.handle.net/11407/435110.1007/978-981-10-4086-3_52reponame:Repositorio Institucional Universidad de Medellíninstname:Universidad de MedellínThis paper presents a framework to characterize the emotional processing of Colombian ex-combatants from illegal groups. The classification process is performed using EEGERP data and behavioral features from psychological tests. The results show that ex-combatant and civilian populations can be automatically separated using supervised techniques.With this, we can provide a decision support system for psychologists to improve current interventions aimed to help ex-combatants to make a successful reintegration to civilian life. © Springer Nature Singapore Pte Ltd. 2017.engSpringer VerlagFacultad de Ingenieríashttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85018389268&doi=10.1007%2f978-981-10-4086-3_52&partnerID=40&md5=bfebea4c0f97c7e3bb2d860a0035a255IFMBE ProceedingsCarlos, T., Agustín, I., Lina, V., Emotional Processing in Colombian Ex-Combatants and Its Relationship with Empathy and Executive Functions Social Neuroscience, 10, pp. 153-165. , PMID: 25302548I-Wei, S., Onton Julie, A., Nitin, P., O’Connell Ryan, M., Simmons Alan, N., Matthews Scott, C., Combat Veterans with PTSD after Mild TBI Exhibit Greater Erps from Posterior-Medial Cortical Areas while Appraising Facial Features, 155, pp. 234-240Light Gregory, A., Williams Lisa, E., Falk, M., Electroencephalography (EEG) and Event-Related Potentials (Erps) with Human Participants Current Protocols in Neuroscience, p. 2524. , CHAPTER:Unit-6Shravani, S., Sinha, V.K., Event-Related Potential: An Overview, 18, p. 70Vilfredo, P.D., Enrica, S., Patrizia, R., Fabiola, V., Personality, Event-Related Potential (ERP) and Heart Rate (HR) in Emotional Word Processing Personality and Individual Differences, 36, pp. 873-891Agustín, I., Hugo, U., Agustín, P., Neural Processing of Emotional Facial and Semantic Expressions in Euthymic Bipolar Disorder (BD) and Its Association with Theory of Mind (Tom) Plos ONE, 7, pp. 1-12Arnaud, D., Scott, M., EEGLAB: An Open Source Toolbox for Analysis of Single-Trial EEG Dynamics including Independent Component Analysis Journal of Neuroscience Methods, 134, pp. 9-21Gismero, E., EHS Escala De Habilidades Sociales Madrid: TEA Publicaciones De Psicología AplicadaAdrian, R., Kenneth, D., Rolf, L., The Reactive-Proactive Aggression Questionnaire: Differential Correlates of Reactive and Proactive Aggression in Adolescent Boysaggressive Behavior, 32, pp. 159-171Key Alexandra, P., Fonaryova, D.G.O., Maguire Mandy, J., Linking brainwaves to the brain An ERP Primer Developmental Neuropsychology, 27, pp. 183-215Pawel, B., Semi-Supervised Feature Extraction Method Using Partial Least Squares and Gaussian Mixture Model Lecture Notes in Engineering and Computer ScienceHerv, A., Partial least squares regression and projection on latent structure regression (PLS Regression) Wiley Interdisciplinary Reviews Computational Statistics, 2, pp. 97-106Padraig, C., Jane, D.S., K-Nearest Neighbour Classifiers Multiple Classifier Systems, pp. 1-17Vapnik Vladimir, N., The Nature of Statistical Learning Theorych Methods of Pattern Recognition, pp. 138-155. , New York, NY: Springer-Verlag2nd edGuo-You, S., Shuang, L., Model Selection of RBF Kernel for C-SVM Based on Genetic Algorithm and Multithreading in 2012 International Conference on Machine Learning and Cybernetics, 1, pp. 382-386ScopusCharacterization framework for Ex-combatants based on EEG and behavioral featuresConference Paperinfo:eu-repo/semantics/conferenceObjecthttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fQuintero-Zea, A., SISTEMIC, Engineering Faculty, Universidad de Medell´ın UDEM, Cra. 87 No. 30 - 65, Medellín, ColombiaSepúlveda-Cano, L.M., ARKADIUS, Engineering Faculty, Universidad de Medell´ın UDEM, Cra. 87 No. 30 - 65, Medellín, ColombiaCalvache, M.R., SISTEMIC, Engineering Faculty, Universidad de Medell´ın UDEM, Cra. 87 No. 30 - 65, Medellín, ColombiaOrrego, S.T., Mental Health Group, School of Public Health, Universidad de Antioquia UDEA, Cra. 53 No. 61-30, Medellín, ColombiaOrrego, N.T., Mental Health Group, School of Public Health, Universidad de Antioquia UDEA, Cra. 53 No. 61-30, Medellín, Colombia, Neuroscience Group, Faculty of Medicine, Universidad de Antioquia UDEA, Medellín, ColombiaQuintero-Zea A.Sepúlveda-Cano L.M.Calvache M.R.Orrego S.T.Orrego N.T.SISTEMIC, Engineering Faculty, Universidad de Medell´ın UDEM, Cra. 87 No. 30 - 65, Medellín, ColombiaARKADIUS, Engineering Faculty, Universidad de Medell´ın UDEM, Cra. 87 No. 30 - 65, Medellín, ColombiaMental Health Group, School of Public Health, Universidad de Antioquia UDEA, Cra. 53 No. 61-30, Medellín, ColombiaNeuroscience Group, Faculty of Medicine, Universidad de Antioquia UDEA, Medellín, ColombiaEmotional processingEmotional recognition taskERPEx-combatantsSupervised learningArtificial intelligenceBiomedical engineeringDecision support systemsEnterprise resource planningSupervised learningTestingBehavioral featuresCivilian populationsClassification processColombiansEmotional recognitionEx-combatantsPsychological testsBehavioral researchThis paper presents a framework to characterize the emotional processing of Colombian ex-combatants from illegal groups. The classification process is performed using EEGERP data and behavioral features from psychological tests. The results show that ex-combatant and civilian populations can be automatically separated using supervised techniques.With this, we can provide a decision support system for psychologists to improve current interventions aimed to help ex-combatants to make a successful reintegration to civilian life. © Springer Nature Singapore Pte Ltd. 2017.http://purl.org/coar/access_right/c_16ec11407/4351oai:repository.udem.edu.co:11407/43512020-05-27 16:34:18.903Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co

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