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Multi-objective metaheuristics for preprocessing EEG data in brain–computer interfaces

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Computación Evolutiva y Redes Neuronales (EVANNAI)es
dc.contributor.authorAler, Ricardo
dc.contributor.authorVega, Alicia
dc.contributor.authorGalván, Inés M.
dc.contributor.authorNebro, Antonio J.
dc.date.accessioned2012-09-04T12:10:09Z
dc.date.accessioned2012-09-04T12:58:19Z
dc.date.available2012-09-04T12:58:19Z
dc.date.issued2012-03
dc.description.abstractIn the field of brain–computer interfaces, one of the main issues is to classify the electroencephalogram (EEG) accurately. EEG signals have a good temporal resolution, but a low spatial one. In this article, metaheuristics are used to compute spatial filters to improve the spatial resolution. Additionally, from a physiological point of view, not all frequency bands are equally relevant. Both spatial filters and relevant frequency bands are user-dependent. In this article a multi-objective formulation for spatial filter optimization and frequency-band selection is proposed. Several multi-objective metaheuristics have been tested for this purpose. The experimental results show, in general, that multi-objective algorithms are able to select a subset of the available frequency bands, while maintaining or improving the accuracy obtained with the whole set. Also, among the different metaheuristics tested, GDE3, which is based on differential evolution, is the most useful algorithm in this context
dc.description.sponsorshipThis work has been funded by the Spanish Ministry of Science under contract TIN2008-06491-C04-03 (MSTAR project).
dc.description.statusPublicado
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.identifier.bibliographicCitationEngineering optimization, Vol.44, No.3 (March 2012), pp. 373–390
dc.identifier.doi10.1080/0305215X.2011.641542
dc.identifier.issn0305-215X
dc.identifier.publicationfirstpage373
dc.identifier.publicationissue3
dc.identifier.publicationlastpage390
dc.identifier.publicationtitleEngineering optimization
dc.identifier.publicationvolume44
dc.identifier.urihttps://hdl.handle.net/10016/15188
dc.identifier.uxxiAR/0000009867
dc.language.isoeng
dc.publisherTaylor & Francis
dc.relation.publisherversionhttp://dx.doi.org/10.1080/0305215X.2011.641542
dc.rights© Taylor & Francis
dc.rights.accessRightsopen access
dc.subject.ecienciaInformática
dc.subject.otherBrain–computer interface
dc.subject.otherMulti-objective optimization
dc.subject.otherEEG filter optimization
dc.titleMulti-objective metaheuristics for preprocessing EEG data in brain–computer interfaces
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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