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dc.contributor.authorDoležal, Jaromír
dc.contributor.authorŠťastný, Jakub
dc.contributor.authorSovka, Pavel
dc.date.accessioned2016-03-01T08:14:52Z
dc.date.available2016-03-01T08:14:52Z
dc.date.issued2011-09cs
dc.identifier.citationRadioengineering. 2011, vol. 20, č. 3, s. 666-676. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/56895
dc.description.abstractThe contribution presents an application of a movement-related EEG temporal development classification which improves the classification score of voluntary movements controlled by closely localized regions of the brain. A dynamic Hidden Markov Model-based (HMM) classifier specifically designed to capture EEG temporal behavior was used. Surprisingly, HMM classifiers are rarely used for BCI design despite of their advantages. Because of this we also experimented with Learning Vector Quantization, Perceptron, and Support Vector Machine classifiers using a feature space which captures the temporal dynamics of the data. The results presented in this work show that HMM achieves the best performance due to an a priori information on physiological behavior of EEG inserted to the HMM classifier. Feature extraction process and problems with classification were analyzed as well. Classification scores of 66.7% – 94.7% were achieved in our experiments.en
dc.formattextcs
dc.format.extent666-676cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2011/11_03_666_676.pdfcs
dc.rightsCreative Commons Attribution 3.0 Unported Licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectBrain-Computer Interfaceen
dc.subjectEEG classificationen
dc.subjectelectroencephalographyen
dc.subjectneural network applicationsen
dc.subjectHidden Markov Modelsen
dc.titleExploiting Temporal Context in High-Resolution Movement-Related EEG Classificationen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
dc.coverage.issue3cs
dc.coverage.volume20cs
dc.rights.accessopenAccessen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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