Emotion recognition systems with electrodermal activity

Affective computing is an interdisciplinary field that aims to automatically recognize and interpret emotions. Recent research has focused on using physiological signals (e.g., electrodermal activity) to improve emotion recognition. However, the theoretical emotion models that underlie these systems...

Descripción completa

Detalles Bibliográficos
Autores: D'Amelio, Tomás Ariel, Galán, Lorenzo A., Maldonado, Emmanuel Alesandro, Díaz Barquinero, Agustín Ariel, Rodríguez Cuello, Jerónimo, Bruno, Nicolás Marcelo, Tagliazucchi, Enzo, Engemann, Denis Alexander
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:326650
Acceso en línea:https://ddd.uab.cat/record/326650
https://dx.doi.org/urn:doi:10.1016/j.neucom.2025.130831
Access Level:acceso abierto
Palabra clave:Affective computing
Emotion recognition
Electrodermal activity
Emotion models
Systematic review
Meta-analysis
id ES_002dbabae1c5a17ca8eb71812f16f0ab
oai_identifier_str oai:ddd.uab.cat:326650
network_acronym_str ES
network_name_str España
repository_id_str
spelling Emotion recognition systems with electrodermal activityFrom affective science to affective computingD'Amelio, Tomás ArielGalán, Lorenzo A.Maldonado, Emmanuel AlesandroDíaz Barquinero, Agustín ArielRodríguez Cuello, JerónimoBruno, Nicolás MarceloTagliazucchi, EnzoEngemann, Denis AlexanderAffective computingEmotion recognitionElectrodermal activityEmotion modelsSystematic reviewMeta-analysisAffective computing is an interdisciplinary field that aims to automatically recognize and interpret emotions. Recent research has focused on using physiological signals (e.g., electrodermal activity) to improve emotion recognition. However, the theoretical emotion models that underlie these systems have received comparatively little attention. We conducted a systematic review and meta-analysis on electrodermal-activity-based emotion-recognition systems. Our findings suggest that arousal prediction models outperform valence prediction models, supporting our preregistered hypothesis. This correlates with arousal's association with autonomic nervous system activity and its direct link to electrodermal activity. We also observed a mismatch between the machine-learning approaches most often used-chiefly classification models-and the predominantly dimensional emotion frameworks adopted in the literature. Specifically, although dimensional affective models are increasingly popular, there has not been a parallel rise in regression models that would better reflect the continuous nature of the underlying data. We conclude that a comprehensive understanding of affective states requires consideration of both psychological and computational perspectives in affective computing research. 22025-01-0120252025-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/326650https://dx.doi.org/urn:doi:10.1016/j.neucom.2025.130831reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengEuropean Commission https://doi.org/10.13039/501100000780 101126533open accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:3266502026-06-06T12:50:31Z
dc.title.none.fl_str_mv Emotion recognition systems with electrodermal activity
From affective science to affective computing
title Emotion recognition systems with electrodermal activity
spellingShingle Emotion recognition systems with electrodermal activity
D'Amelio, Tomás Ariel
Affective computing
Emotion recognition
Electrodermal activity
Emotion models
Systematic review
Meta-analysis
title_short Emotion recognition systems with electrodermal activity
title_full Emotion recognition systems with electrodermal activity
title_fullStr Emotion recognition systems with electrodermal activity
title_full_unstemmed Emotion recognition systems with electrodermal activity
title_sort Emotion recognition systems with electrodermal activity
dc.creator.none.fl_str_mv D'Amelio, Tomás Ariel
Galán, Lorenzo A.
Maldonado, Emmanuel Alesandro
Díaz Barquinero, Agustín Ariel
Rodríguez Cuello, Jerónimo
Bruno, Nicolás Marcelo
Tagliazucchi, Enzo
Engemann, Denis Alexander
author D'Amelio, Tomás Ariel
author_facet D'Amelio, Tomás Ariel
Galán, Lorenzo A.
Maldonado, Emmanuel Alesandro
Díaz Barquinero, Agustín Ariel
Rodríguez Cuello, Jerónimo
Bruno, Nicolás Marcelo
Tagliazucchi, Enzo
Engemann, Denis Alexander
author_role author
author2 Galán, Lorenzo A.
Maldonado, Emmanuel Alesandro
Díaz Barquinero, Agustín Ariel
Rodríguez Cuello, Jerónimo
Bruno, Nicolás Marcelo
Tagliazucchi, Enzo
Engemann, Denis Alexander
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Affective computing
Emotion recognition
Electrodermal activity
Emotion models
Systematic review
Meta-analysis
topic Affective computing
Emotion recognition
Electrodermal activity
Emotion models
Systematic review
Meta-analysis
description Affective computing is an interdisciplinary field that aims to automatically recognize and interpret emotions. Recent research has focused on using physiological signals (e.g., electrodermal activity) to improve emotion recognition. However, the theoretical emotion models that underlie these systems have received comparatively little attention. We conducted a systematic review and meta-analysis on electrodermal-activity-based emotion-recognition systems. Our findings suggest that arousal prediction models outperform valence prediction models, supporting our preregistered hypothesis. This correlates with arousal's association with autonomic nervous system activity and its direct link to electrodermal activity. We also observed a mismatch between the machine-learning approaches most often used-chiefly classification models-and the predominantly dimensional emotion frameworks adopted in the literature. Specifically, although dimensional affective models are increasingly popular, there has not been a parallel rise in regression models that would better reflect the continuous nature of the underlying data. We conclude that a comprehensive understanding of affective states requires consideration of both psychological and computational perspectives in affective computing research.
publishDate 2025
dc.date.none.fl_str_mv 2
2025-01-01
2025
2025-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/326650
https://dx.doi.org/urn:doi:10.1016/j.neucom.2025.130831
url https://ddd.uab.cat/record/326650
https://dx.doi.org/urn:doi:10.1016/j.neucom.2025.130831
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission https://doi.org/10.13039/501100000780 101126533
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869402477161349120
score 15,812455

Similares en LA Referencia