Multi-lag analysis of symbolic entropies on EEG recordings for distress recognition

Autores
Martínez Rodrigo, Arturo; García Martínez, Beatriz; Zunino, Luciano José; Alcaraz, Raúl; Fernández Caballero, Antonio
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Distress is a critical problem in developed societies given its long-term negative effects on physical and mental health. The interest in studying this emotion has notably increased during last years, being electroencephalography (EEG) signals preferred over other physiological variables in this research field. In addition, the non-stationary nature of brain dynamics has impulsed the use of non-linear metrics, such as symbolic entropies in brain signal analysis. Thus, the influence of time-lag on brain patterns assessment has not been tested. Hence, in the present study two permutation entropies denominated Delayed Permutation Entropy and Permutation Min-Entropy have been computed for the first time at different time-lags to discern between emotional states of calmness and distress from EEG signals. Moreover, a number of curve-related features were also calculated to assess brain dynamics across different temporal intervals. Complementary information among these variables was studied through sequential forward selection and 10-fold cross-validation approaches. According to the results obtained, the multi-lag entropy analysis has been able to reveal new significant insights so far undiscovered, thus notably improving the process of distress recognition from EEG recordings.
Fil: Martínez Rodrigo, Arturo. Universidad de Castilla-La Mancha; España
Fil: García Martínez, Beatriz. Universidad de Castilla-La Mancha; España
Fil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería; Argentina
Fil: Alcaraz, Raúl. Universidad de Castilla-La Mancha; España
Fil: Fernández Caballero, Antonio. Biomedical Research Networking Centre in Mental Health; España. Universidad de Castilla-La Mancha; España
Materia
DELAYED PERMUTATION ENTROPY
DISTRESS
ELECTROENCEPHALOGRAPHY
NON-LINEAR METRICS
PERMUTATION MIN-ENTROPY
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/122512
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Multi-lag analysis of symbolic entropies on EEG recordings for distress recognitionMartínez Rodrigo, ArturoGarcía Martínez, BeatrizZunino, Luciano JoséAlcaraz, RaúlFernández Caballero, AntonioDELAYED PERMUTATION ENTROPYDISTRESSELECTROENCEPHALOGRAPHYNON-LINEAR METRICSPERMUTATION MIN-ENTROPYhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Distress is a critical problem in developed societies given its long-term negative effects on physical and mental health. The interest in studying this emotion has notably increased during last years, being electroencephalography (EEG) signals preferred over other physiological variables in this research field. In addition, the non-stationary nature of brain dynamics has impulsed the use of non-linear metrics, such as symbolic entropies in brain signal analysis. Thus, the influence of time-lag on brain patterns assessment has not been tested. Hence, in the present study two permutation entropies denominated Delayed Permutation Entropy and Permutation Min-Entropy have been computed for the first time at different time-lags to discern between emotional states of calmness and distress from EEG signals. Moreover, a number of curve-related features were also calculated to assess brain dynamics across different temporal intervals. Complementary information among these variables was studied through sequential forward selection and 10-fold cross-validation approaches. According to the results obtained, the multi-lag entropy analysis has been able to reveal new significant insights so far undiscovered, thus notably improving the process of distress recognition from EEG recordings.Fil: Martínez Rodrigo, Arturo. Universidad de Castilla-La Mancha; EspañaFil: García Martínez, Beatriz. Universidad de Castilla-La Mancha; EspañaFil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería; ArgentinaFil: Alcaraz, Raúl. Universidad de Castilla-La Mancha; EspañaFil: Fernández Caballero, Antonio. Biomedical Research Networking Centre in Mental Health; España. Universidad de Castilla-La Mancha; EspañaFrontiers Media S.A.2019-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/122512Martínez Rodrigo, Arturo; García Martínez, Beatriz; Zunino, Luciano José; Alcaraz, Raúl; Fernández Caballero, Antonio; Multi-lag analysis of symbolic entropies on EEG recordings for distress recognition; Frontiers Media S.A.; Frontiers in Neuroinformatics; 13; 5-2019; 1-151662-5196CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/article/10.3389/fninf.2019.00040/fullinfo:eu-repo/semantics/altIdentifier/doi/10.3389/fninf.2019.00040info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:11:50Zoai:ri.conicet.gov.ar:11336/122512instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:11:50.974CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Multi-lag analysis of symbolic entropies on EEG recordings for distress recognition
title Multi-lag analysis of symbolic entropies on EEG recordings for distress recognition
spellingShingle Multi-lag analysis of symbolic entropies on EEG recordings for distress recognition
Martínez Rodrigo, Arturo
DELAYED PERMUTATION ENTROPY
DISTRESS
ELECTROENCEPHALOGRAPHY
NON-LINEAR METRICS
PERMUTATION MIN-ENTROPY
title_short Multi-lag analysis of symbolic entropies on EEG recordings for distress recognition
title_full Multi-lag analysis of symbolic entropies on EEG recordings for distress recognition
title_fullStr Multi-lag analysis of symbolic entropies on EEG recordings for distress recognition
title_full_unstemmed Multi-lag analysis of symbolic entropies on EEG recordings for distress recognition
title_sort Multi-lag analysis of symbolic entropies on EEG recordings for distress recognition
dc.creator.none.fl_str_mv Martínez Rodrigo, Arturo
García Martínez, Beatriz
Zunino, Luciano José
Alcaraz, Raúl
Fernández Caballero, Antonio
author Martínez Rodrigo, Arturo
author_facet Martínez Rodrigo, Arturo
García Martínez, Beatriz
Zunino, Luciano José
Alcaraz, Raúl
Fernández Caballero, Antonio
author_role author
author2 García Martínez, Beatriz
Zunino, Luciano José
Alcaraz, Raúl
Fernández Caballero, Antonio
author2_role author
author
author
author
dc.subject.none.fl_str_mv DELAYED PERMUTATION ENTROPY
DISTRESS
ELECTROENCEPHALOGRAPHY
NON-LINEAR METRICS
PERMUTATION MIN-ENTROPY
topic DELAYED PERMUTATION ENTROPY
DISTRESS
ELECTROENCEPHALOGRAPHY
NON-LINEAR METRICS
PERMUTATION MIN-ENTROPY
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Distress is a critical problem in developed societies given its long-term negative effects on physical and mental health. The interest in studying this emotion has notably increased during last years, being electroencephalography (EEG) signals preferred over other physiological variables in this research field. In addition, the non-stationary nature of brain dynamics has impulsed the use of non-linear metrics, such as symbolic entropies in brain signal analysis. Thus, the influence of time-lag on brain patterns assessment has not been tested. Hence, in the present study two permutation entropies denominated Delayed Permutation Entropy and Permutation Min-Entropy have been computed for the first time at different time-lags to discern between emotional states of calmness and distress from EEG signals. Moreover, a number of curve-related features were also calculated to assess brain dynamics across different temporal intervals. Complementary information among these variables was studied through sequential forward selection and 10-fold cross-validation approaches. According to the results obtained, the multi-lag entropy analysis has been able to reveal new significant insights so far undiscovered, thus notably improving the process of distress recognition from EEG recordings.
Fil: Martínez Rodrigo, Arturo. Universidad de Castilla-La Mancha; España
Fil: García Martínez, Beatriz. Universidad de Castilla-La Mancha; España
Fil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería; Argentina
Fil: Alcaraz, Raúl. Universidad de Castilla-La Mancha; España
Fil: Fernández Caballero, Antonio. Biomedical Research Networking Centre in Mental Health; España. Universidad de Castilla-La Mancha; España
description Distress is a critical problem in developed societies given its long-term negative effects on physical and mental health. The interest in studying this emotion has notably increased during last years, being electroencephalography (EEG) signals preferred over other physiological variables in this research field. In addition, the non-stationary nature of brain dynamics has impulsed the use of non-linear metrics, such as symbolic entropies in brain signal analysis. Thus, the influence of time-lag on brain patterns assessment has not been tested. Hence, in the present study two permutation entropies denominated Delayed Permutation Entropy and Permutation Min-Entropy have been computed for the first time at different time-lags to discern between emotional states of calmness and distress from EEG signals. Moreover, a number of curve-related features were also calculated to assess brain dynamics across different temporal intervals. Complementary information among these variables was studied through sequential forward selection and 10-fold cross-validation approaches. According to the results obtained, the multi-lag entropy analysis has been able to reveal new significant insights so far undiscovered, thus notably improving the process of distress recognition from EEG recordings.
publishDate 2019
dc.date.none.fl_str_mv 2019-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/122512
Martínez Rodrigo, Arturo; García Martínez, Beatriz; Zunino, Luciano José; Alcaraz, Raúl; Fernández Caballero, Antonio; Multi-lag analysis of symbolic entropies on EEG recordings for distress recognition; Frontiers Media S.A.; Frontiers in Neuroinformatics; 13; 5-2019; 1-15
1662-5196
CONICET Digital
CONICET
url http://hdl.handle.net/11336/122512
identifier_str_mv Martínez Rodrigo, Arturo; García Martínez, Beatriz; Zunino, Luciano José; Alcaraz, Raúl; Fernández Caballero, Antonio; Multi-lag analysis of symbolic entropies on EEG recordings for distress recognition; Frontiers Media S.A.; Frontiers in Neuroinformatics; 13; 5-2019; 1-15
1662-5196
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/article/10.3389/fninf.2019.00040/full
info:eu-repo/semantics/altIdentifier/doi/10.3389/fninf.2019.00040
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Frontiers Media S.A.
publisher.none.fl_str_mv Frontiers Media S.A.
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842270172834955264
score 13.13397

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