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Mixtures of independent component analyzers for EEG prediction

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Mixtures of independent component analyzers for EEG prediction

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dc.contributor.author Safont Armero, Gonzalo es_ES
dc.contributor.author Salazar Afanador, Addisson es_ES
dc.contributor.author Vergara Domínguez, Luís es_ES
dc.contributor.author Gonzalez, Alberto es_ES
dc.contributor.author Vidal Maciá, Antonio Manuel es_ES
dc.date.accessioned 2014-02-06T08:12:15Z
dc.date.issued 2012
dc.identifier.isbn 978-3-642-35250-8
dc.identifier.issn 1865-0929
dc.identifier.uri http://hdl.handle.net/10251/35381
dc.description.abstract This paper presents a new application of independent component analysis mixture modeling (ICAMM) for prediction of electroencephalographic (EEG) signals. Demonstrations in prediction of missing EEG data in a working memory task using classic methods and an ICAMM-based algorithm are included. The performance of the methods is measured by using four error indicators: signal-to-interference (SIR) ratio, Kullback-Leibler divergence, correlation at lag zero and mean structural similarity index. The results show that the ICAMM-based algorithm outperforms the classical spherical splines method which is commonly used in EEG signal processing. Hence, the potential of using mixtures of independent component analyzers (ICAs) to improve prediction, as opposed on estimating only one ICA is demonstrated. es_ES
dc.description.sponsorship This work has been supported by Generalitat Valenciana under grants PROMETEO/2010/040 and ISIC/2012/006
dc.format.extent 8 es_ES
dc.language Inglés es_ES
dc.publisher Springer Verlag (Germany) es_ES
dc.relation.ispartof Green and smart technology with sensor applications es_ES
dc.relation.ispartofseries Communications in Computer and Information Science;338
dc.rights Reserva de todos los derechos es_ES
dc.subject Electroencefalogramas es_ES
dc.subject ICA es_ES
dc.subject Predicción es_ES
dc.subject Tareas de memoria es_ES
dc.subject EEG es_ES
dc.subject Prediction es_ES
dc.subject Working-memory task es_ES
dc.subject.classification CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL es_ES
dc.subject.classification TEORIA DE LA SEÑAL Y COMUNICACIONES es_ES
dc.title Mixtures of independent component analyzers for EEG prediction es_ES
dc.type Capítulo de libro es_ES
dc.embargo.lift 10000-01-01
dc.embargo.terms forever es_ES
dc.identifier.doi 10.1007/978-3-642-35251-5_46
dc.relation.projectID info:eu-repo/grantAgreement/GVA//PROMETEO%2F2010%2F040/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//ISIC%2F2012%2F006/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Telecomunicación y Aplicaciones Multimedia - Institut Universitari de Telecomunicacions i Aplicacions Multimèdia es_ES
dc.description.bibliographicCitation Safont Armero, G.; Salazar Afanador, A.; Vergara Domínguez, L.; Gonzalez, A.; Vidal Maciá, AM. (2012). Mixtures of independent component analyzers for EEG prediction. En Green and smart technology with sensor applications. Springer Verlag (Germany). 338:328-335. https://doi.org/10.1007/978-3-642-35251-5_46 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename International Conferences, GST and SIA 2012 es_ES
dc.relation.conferencedate November 28-December 2, 2012 es_ES
dc.relation.conferenceplace Jeju Island, Korea es_ES
dc.relation.publisherversion http://link.springer.com/chapter/10.1007%2F978-3-642-35251-5_46 es_ES
dc.description.upvformatpinicio 328 es_ES
dc.description.upvformatpfin 335 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 338 es_ES
dc.relation.senia 234571
dc.contributor.funder Generalitat Valenciana
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