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.
Centro de Investigaciones Ópticas
Materia
Física
electroencephalography
distress
non-linear metrics
delayed permutation entropy
permutation min-entropy
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/108120
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/108120
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Multi-Lag Analysis of Symbolic Entropies on EEG Recordings for Distress RecognitionMartínez Rodrigo, ArturoGarcía Martínez, BeatrizZunino, Luciano JoséAlcaraz, RaúlFernández Caballero, AntonioFísicaelectroencephalographydistressnon-linear metricsdelayed permutation entropypermutation min-entropyDistress 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.Centro de Investigaciones Ópticas2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/108120enginfo:eu-repo/semantics/altIdentifier/url/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC6558149&blobtype=pdfinfo:eu-repo/semantics/altIdentifier/issn/1662-5196info:eu-repo/semantics/altIdentifier/pmid/31214006info:eu-repo/semantics/altIdentifier/doi/10.3389/fninf.2019.00040info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:56:07Zoai:sedici.unlp.edu.ar:10915/108120Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:56:07.765SEDICI (UNLP) - Universidad Nacional de La Platafalse
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
Física
electroencephalography
distress
non-linear metrics
delayed permutation entropy
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 Física
electroencephalography
distress
non-linear metrics
delayed permutation entropy
permutation min-entropy
topic Física
electroencephalography
distress
non-linear metrics
delayed permutation entropy
permutation min-entropy
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.
Centro de Investigaciones Ópticas
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
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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://sedici.unlp.edu.ar/handle/10915/108120
url http://sedici.unlp.edu.ar/handle/10915/108120
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC6558149&blobtype=pdf
info:eu-repo/semantics/altIdentifier/issn/1662-5196
info:eu-repo/semantics/altIdentifier/pmid/31214006
info:eu-repo/semantics/altIdentifier/doi/10.3389/fninf.2019.00040
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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