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dc.contributor.authorAquino Brítez, Diego
dc.contributor.authorOrtiz, Andrés
dc.contributor.authorOrtega Lopera, Julio 
dc.contributor.authorEscobar Pérez, Juan José 
dc.contributor.authorFormoso, Marco
dc.contributor.authorGan, John Q.
dc.contributor.authorEscobar Pérez, Juan José 
dc.date.accessioned2021-05-26T11:23:43Z
dc.date.available2021-05-26T11:23:43Z
dc.date.issued2021-03-17
dc.identifier.citationAquino-Brítez, D.; Ortiz, A.; Ortega, J.; León, J.; Formoso, M.; Gan, J.Q.; Escobar, J.J. Optimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary Algorithms. Sensors 2021, 21, 2096. https:// doi.org/10.3390/s21062096es_ES
dc.identifier.urihttp://hdl.handle.net/10481/68749
dc.description.abstractElectroencephalography (EEG) signal classification is a challenging task due to the low signal-to-noise ratio and the usual presence of artifacts from different sources. Different classification techniques, which are usually based on a predefined set of features extracted from the EEG band power distribution profile, have been previously proposed. However, the classification of EEG still remains a challenge, depending on the experimental conditions and the responses to be captured. In this context, the use of deep neural networks offers new opportunities to improve the classification performance without the use of a predefined set of features. Nevertheless, Deep Learning architectures include a vast number of hyperparameters on which the performance of the model relies. In this paper, we propose a method for optimizing Deep Learning models, not only the hyperparameters, but also their structure, which is able to propose solutions that consist of different architectures due to different layer combinations. The experimental results corroborate that deep architectures optimized by our method outperform the baseline approaches and result in computationally efficient models. Moreover, we demonstrate that optimized architectures improve the energy efficiency with respect to the baseline models.es_ES
dc.description.sponsorshipMINECO/FEDER under PGC2018-098813- B-C31 and PGC2018-098813-B-C32 projectses_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectBrain-computer interfaces (BCI)es_ES
dc.subjectEvolutionary computinges_ES
dc.subjectMulti-objective EEG classificationes_ES
dc.subjectDeep learninges_ES
dc.titleOptimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary Algorithmses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3390/s21062096


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Atribución 3.0 España
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