Affective recognition from EEG signals: an integrated data-mining approach

Emotions play an important role in human communication, interaction, and decision making processes. Therefore, considerable efforts have been made towards the automatic identification of human emotions, in particular electroencephalogram (EEG) signals and Data Mining (DM) techniques have been then u...

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Autores:
Mendoza Palechor, Fabio Enrique
Recena Menezes, Maria Luiza
Sant’anna, Anita
Ortiz Barrios, Miguel Angel
Samara, Anas
Galway, Leo
Tipo de recurso:
Article of journal
Fecha de publicación:
2018
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/1747
Acceso en línea:
https://hdl.handle.net/11323/1747
https://doi.org/10.1007/s12652-018-1065-z
https://repositorio.cuc.edu.co/
Palabra clave:
Affective computing
Affective recognition
Data Mining (DM)
Electroencephalogram (EEG)
Statistical features
Rights
openAccess
License
Atribución – No comercial – Compartir igual
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dc.title.eng.fl_str_mv Affective recognition from EEG signals: an integrated data-mining approach
title Affective recognition from EEG signals: an integrated data-mining approach
spellingShingle Affective recognition from EEG signals: an integrated data-mining approach
Affective computing
Affective recognition
Data Mining (DM)
Electroencephalogram (EEG)
Statistical features
title_short Affective recognition from EEG signals: an integrated data-mining approach
title_full Affective recognition from EEG signals: an integrated data-mining approach
title_fullStr Affective recognition from EEG signals: an integrated data-mining approach
title_full_unstemmed Affective recognition from EEG signals: an integrated data-mining approach
title_sort Affective recognition from EEG signals: an integrated data-mining approach
dc.creator.fl_str_mv Mendoza Palechor, Fabio Enrique
Recena Menezes, Maria Luiza
Sant’anna, Anita
Ortiz Barrios, Miguel Angel
Samara, Anas
Galway, Leo
dc.contributor.author.spa.fl_str_mv Mendoza Palechor, Fabio Enrique
Recena Menezes, Maria Luiza
Sant’anna, Anita
Ortiz Barrios, Miguel Angel
Samara, Anas
Galway, Leo
dc.subject.eng.fl_str_mv Affective computing
Affective recognition
Data Mining (DM)
Electroencephalogram (EEG)
Statistical features
topic Affective computing
Affective recognition
Data Mining (DM)
Electroencephalogram (EEG)
Statistical features
description Emotions play an important role in human communication, interaction, and decision making processes. Therefore, considerable efforts have been made towards the automatic identification of human emotions, in particular electroencephalogram (EEG) signals and Data Mining (DM) techniques have been then used to create models recognizing the affective states of users. However, most previous works have used clinical grade EEG systems with at least 32 electrodes. These systems are expensive and cumbersome, and therefore unsuitable for usage during normal daily activities. Smaller EEG headsets such as the Emotiv are now available and can be used during daily activities. This paper investigates the accuracy and applicability of previous affective recognition methods on data collected with an Emotiv headset while participants used a personal computer to fulfill several tasks. Several features were extracted from four channels only (AF3, AF4, F3 and F4 in accordance with the 10–20 system). Both Support Vector Machine and Naïve Bayes were used for emotion classification. Results demonstrate that such methods can be used to accurately detect emotions using a small EEG headset during a normal daily activity.
publishDate 2018
dc.date.accessioned.none.fl_str_mv 2018-11-23T16:10:24Z
dc.date.available.none.fl_str_mv 2018-11-23T16:10:24Z
dc.date.issued.none.fl_str_mv 2018
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.spa.fl_str_mv 18685137
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/1747
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/s12652-018-1065-z
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 18685137
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/1747
https://doi.org/10.1007/s12652-018-1065-z
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.rights.spa.fl_str_mv Atribución – No comercial – Compartir igual
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
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rights_invalid_str_mv Atribución – No comercial – Compartir igual
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.publisher.spa.fl_str_mv Journal of Ambient Intelligence and Humanized Computing
institution Corporación Universidad de la Costa
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spelling Mendoza Palechor, Fabio EnriqueRecena Menezes, Maria LuizaSant’anna, AnitaOrtiz Barrios, Miguel AngelSamara, AnasGalway, Leo2018-11-23T16:10:24Z2018-11-23T16:10:24Z201818685137https://hdl.handle.net/11323/1747https://doi.org/10.1007/s12652-018-1065-zCorporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Emotions play an important role in human communication, interaction, and decision making processes. Therefore, considerable efforts have been made towards the automatic identification of human emotions, in particular electroencephalogram (EEG) signals and Data Mining (DM) techniques have been then used to create models recognizing the affective states of users. However, most previous works have used clinical grade EEG systems with at least 32 electrodes. These systems are expensive and cumbersome, and therefore unsuitable for usage during normal daily activities. Smaller EEG headsets such as the Emotiv are now available and can be used during daily activities. This paper investigates the accuracy and applicability of previous affective recognition methods on data collected with an Emotiv headset while participants used a personal computer to fulfill several tasks. Several features were extracted from four channels only (AF3, AF4, F3 and F4 in accordance with the 10–20 system). Both Support Vector Machine and Naïve Bayes were used for emotion classification. Results demonstrate that such methods can be used to accurately detect emotions using a small EEG headset during a normal daily activity.Mendoza Palechor, Fabio Enrique-0000-0002-2755-0841-600Recena Menezes, Maria Luiza-c8eaba4a-0b49-447d-90b0-5cf0437500c0-0Sant’anna, Anita-f2c553ab-b317-42ef-bccc-7093c0134552-0Ortiz Barrios, Miguel Angel-0000-0001-6890-7547-600Samara, Anas-edeac690-26ba-4f55-82b3-b60ba7eba410-0Galway, Leo-893471ae-82ef-4b5c-8835-f7cee402d3b1-0engJournal of Ambient Intelligence and Humanized ComputingAtribución – No comercial – Compartir igualinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Affective computingAffective recognitionData Mining (DM)Electroencephalogram (EEG)Statistical featuresAffective recognition from EEG signals: an integrated data-mining approachArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionPublicationORIGINALAffective recognition from EEG signals.pdfAffective recognition from EEG signals.pdfapplication/pdf105257https://repositorio.cuc.edu.co/bitstreams/b92da808-37d9-4a94-be34-88dd0365d728/downloadfd9b4a51899764fbeab44551c706f3dbMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/dd9c088c-77d4-43ee-8d73-4b2f7d21eb2c/download8a4605be74aa9ea9d79846c1fba20a33MD52THUMBNAILAffective recognition from EEG signals.pdf.jpgAffective recognition from EEG signals.pdf.jpgimage/jpeg47850https://repositorio.cuc.edu.co/bitstreams/19af6a64-6b37-4b5c-a388-1a78e02461be/downloadcab28bac1837d2c3d114db1966de5461MD54TEXTAffective recognition from EEG signals.pdf.txtAffective recognition from EEG signals.pdf.txttext/plain1596https://repositorio.cuc.edu.co/bitstreams/48c43be5-fe8d-4975-ae77-3fd4f767345f/download236996fd93b5f6553f167c905cac2906MD5511323/1747oai:repositorio.cuc.edu.co:11323/17472024-09-17 11:00:57.774open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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