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dc.contributor.authorSantana Hermida, Roberto
dc.date.accessioned2013-12-18T10:56:46Z
dc.date.available2013-12-18T10:56:46Z
dc.date.issued2013-12-18T10:56:46Z
dc.identifier.urihttp://hdl.handle.net/10810/11154
dc.description.abstractAccurate and fast decoding of speech imagery from electroencephalographic (EEG) data could serve as a basis for a new generation of brain computer interfaces (BCIs), more portable and easier to use. However, decoding of speech imagery from EEG is a hard problem due to many factors. In this paper we focus on the analysis of the classification step of speech imagery decoding for a three-class vowel speech imagery recognition problem. We empirically show that different classification subtasks may require different classifiers for accurately decoding and obtain a classification accuracy that improves the best results previously published. We further investigate the relationship between the classifiers and different sets of features selected by the common spatial patterns method. Our results indicate that further improvement on BCIs based on speech imagery could be achieved by carefully selecting an appropriate combination of classifiers for the subtasks involved.es
dc.language.isoenges
dc.relation.ispartofseriesEHU-KZAA-TR;2013-02
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.subjectspeech imageryes
dc.subjectbrain computer interfacees
dc.subjectclassification methodses
dc.titleA detailed investigation of classification methods for vowel speech imagery recognitiones
dc.typeinfo:eu-repo/semantics/reportes
dc.departamentoesCiencia de la computación e inteligencia artificiales_ES
dc.departamentoeuKonputazio zientziak eta adimen artifizialaes_ES


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