Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/73270
Campo DC Valoridioma
dc.contributor.authorFourati, Rahmaen_US
dc.contributor.authorAmmar, Boudouren_US
dc.contributor.authorSánchez Medina, Javier Jesúsen_US
dc.contributor.authorAlimi, Adel M.en_US
dc.date.accessioned2020-06-15T10:54:53Z-
dc.date.available2020-06-15T10:54:53Z-
dc.date.issued2022en_US
dc.identifier.issn1949-3045en_US
dc.identifier.otherScopus-
dc.identifier.urihttp://hdl.handle.net/10553/73270-
dc.description.abstractIn real-world applications such as emotion recognition from recorded brain activity, data are captured from electrodes over time. These signals constitute a multidimensional time series. In this paper, Echo State Network (ESN), a recurrent neural network with great success in time series prediction and classification, is optimized with different neural plasticity rules for classification of emotions based on electroencephalogram (EEG) time series. The developed network could automatically extract valid features from EEG signals. We use the filtered signals as the network input and do not take any feature extraction methods. Evaluated on two well-known benchmarks, the DEAP dataset, and the SEED dataset, the performance of the ESN with intrinsic plasticity greatly outperforms the feature-based methods and shows certain advantages compared with other existing methods. Thus, the proposed network can form a more complete and efficient representation, whilst retaining the advantages such as faster learning speed and more reliable performance.en_US
dc.languageengen_US
dc.relation.ispartofIEEE Transactions on Affective Computingen_US
dc.sourceIEEE Transactions on Affective Computing [EISSN 1949-3045], v. 13(2), p. 972-984 (2022)en_US
dc.subject120304 Inteligencia artificialen_US
dc.subject.otherBrain modelingen_US
dc.subject.otherEcho state networken_US
dc.subject.otherElectroencephalogramen_US
dc.subject.otherElectroencephalographyen_US
dc.subject.otherEmotion recognitionen_US
dc.subject.otherErbiumen_US
dc.subject.otherFeature extractionen_US
dc.subject.otherFeature learningen_US
dc.subject.otherIntrinsic plasticityen_US
dc.subject.otherReservoirsen_US
dc.subject.otherSynaptic plasticityen_US
dc.subject.otherTask analysisen_US
dc.titleUnsupervised learning in reservoir computing for EEG-based emotion recognitionen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typearticleen_US
dc.identifier.doi10.1109/TAFFC.2020.2982143en_US
dc.identifier.scopus85085978292-
dc.contributor.authorscopusid44961198800-
dc.contributor.authorscopusid23974208100-
dc.contributor.authorscopusid26421466600-
dc.contributor.authorscopusid7003687617-
dc.identifier.eissn1949-3045-
dc.description.lastpage984en_US
dc.identifier.issue2-
dc.description.firstpage972en_US
dc.relation.volume13en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.utils.revisionen_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-INFen_US
dc.description.sjr1,905
dc.description.jcr11,2
dc.description.sjrqQ1
dc.description.jcrqQ1
dc.description.scieSCIE
dc.description.miaricds10,5
item.fulltextSin texto completo-
item.grantfulltextnone-
crisitem.author.deptGIR IUCES: Centro de Innovación para la Empresa, el Turismo, la Internacionalización y la Sostenibilidad-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0003-2530-3182-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.fullNameSánchez Medina, Javier Jesús-
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