Recognition of Emotional States from EEG Signals with Nonlinear Regularity- and Predictability-Based Entropy Metrics

Recently, the recognition of emotions with electroencephalographic (EEG) signals has received increasing attention. Furthermore, the nonstationarity of brain has intensified the application of nonlinear methods. Nonetheless, metrics like quadratic sample entropy (QSE), amplitude-aware permutation en...

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Detalles Bibliográficos
Autores principales: García-Martínez, Beatriz, Fernández-Caballero, Antonio, Zunino, Luciano José, Martínez-Rodrigo, Arturo
Formato: Articulo
Lenguaje:Inglés
Publicado: 2021
Materias:
Ingeniería
Informática
Emotions
Electroencephalography
Entropy metrics
Nonlinear analysis
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/131625
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-131625
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ingeniería
Informática
Emotions
Electroencephalography
Entropy metrics
Nonlinear analysis
spellingShingle Ingeniería
Informática
Emotions
Electroencephalography
Entropy metrics
Nonlinear analysis
García-Martínez, Beatriz
Fernández-Caballero, Antonio
Zunino, Luciano José
Martínez-Rodrigo, Arturo
Recognition of Emotional States from EEG Signals with Nonlinear Regularity- and Predictability-Based Entropy Metrics
topic_facet Ingeniería
Informática
Emotions
Electroencephalography
Entropy metrics
Nonlinear analysis
description Recently, the recognition of emotions with electroencephalographic (EEG) signals has received increasing attention. Furthermore, the nonstationarity of brain has intensified the application of nonlinear methods. Nonetheless, metrics like quadratic sample entropy (QSE), amplitude-aware permutation entropy (AAPE) and permutation min-entropy (PME) have never been applied to discern between more than two emotions. Therefore, this study computes for the first time QSE, AAPE and PME for recognition of four groups of emotions. After preprocessing the EEG recordings, the three entropy metrics were computed. Then, a tenfold classification approach based on a sequential forward selection scheme and a support vector machine classifier was implemented. This procedure was applied in a multi-class scheme including the four groups of study simultaneously, and in a binary-class approach for discerning emotions two by two, regarding their levels of arousal and valence. For both schemes, QSE+AAPE and QSE+PME were combined. In both multi-class and binary-class schemes, the best results were obtained in frontal and parietal brain areas. Furthermore, in most of the cases channels from QSE and AAPE/PME were selected in the classification models, thus highlighting the complementarity between those different types of entropy indices and achieving global accuracy results higher than 90% in multi-class and binary-class schemes. The combination of regularity- and predictability-based entropy indices denoted a high degree of complementarity between those nonlinear methods. Finally, the relevance of frontal and parietal areas for recognition of emotions has revealed the essential role of those brain regions in emotional processes.
format Articulo
Articulo
author García-Martínez, Beatriz
Fernández-Caballero, Antonio
Zunino, Luciano José
Martínez-Rodrigo, Arturo
author_facet García-Martínez, Beatriz
Fernández-Caballero, Antonio
Zunino, Luciano José
Martínez-Rodrigo, Arturo
author_sort García-Martínez, Beatriz
title Recognition of Emotional States from EEG Signals with Nonlinear Regularity- and Predictability-Based Entropy Metrics
title_short Recognition of Emotional States from EEG Signals with Nonlinear Regularity- and Predictability-Based Entropy Metrics
title_full Recognition of Emotional States from EEG Signals with Nonlinear Regularity- and Predictability-Based Entropy Metrics
title_fullStr Recognition of Emotional States from EEG Signals with Nonlinear Regularity- and Predictability-Based Entropy Metrics
title_full_unstemmed Recognition of Emotional States from EEG Signals with Nonlinear Regularity- and Predictability-Based Entropy Metrics
title_sort recognition of emotional states from eeg signals with nonlinear regularity- and predictability-based entropy metrics
publishDate 2021
url http://sedici.unlp.edu.ar/handle/10915/131625
work_keys_str_mv AT garciamartinezbeatriz recognitionofemotionalstatesfromeegsignalswithnonlinearregularityandpredictabilitybasedentropymetrics
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AT zuninolucianojose recognitionofemotionalstatesfromeegsignalswithnonlinearregularityandpredictabilitybasedentropymetrics
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