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dc.contributor.author
Martínez Rodrigo, Arturo
dc.contributor.author
García Martínez, Beatriz
dc.contributor.author
Zunino, Luciano José
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dc.contributor.author
Alcaraz, Raúl
dc.contributor.author
Fernández Caballero, Antonio
dc.date.available
2021-01-12T17:26:07Z
dc.date.issued
2019-05
dc.identifier.citation
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
dc.identifier.issn
1662-5196
dc.identifier.uri
http://hdl.handle.net/11336/122512
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Frontiers Media S.A.
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
DELAYED PERMUTATION ENTROPY
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DISTRESS
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ELECTROENCEPHALOGRAPHY
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NON-LINEAR METRICS
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PERMUTATION MIN-ENTROPY
dc.subject.classification
Otras Ciencias Físicas
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dc.subject.classification
Ciencias Físicas
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Multi-lag analysis of symbolic entropies on EEG recordings for distress recognition
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-11-25T16:45:17Z
dc.journal.volume
13
dc.journal.pagination
1-15
dc.journal.pais
Suiza
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dc.journal.ciudad
Lausana
dc.description.fil
Fil: Martínez Rodrigo, Arturo. Universidad de Castilla-La Mancha; España
dc.description.fil
Fil: García Martínez, Beatriz. Universidad de Castilla-La Mancha; España
dc.description.fil
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
dc.description.fil
Fil: Alcaraz, Raúl. Universidad de Castilla-La Mancha; España
dc.description.fil
Fil: Fernández Caballero, Antonio. Biomedical Research Networking Centre in Mental Health; España. Universidad de Castilla-La Mancha; España
dc.journal.title
Frontiers in Neuroinformatics
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/article/10.3389/fninf.2019.00040/full
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.3389/fninf.2019.00040
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