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dc.contributor.authorŠťastný, Jakub
dc.contributor.authorSovka, Pavel
dc.contributor.authorStancak, A.
dc.date.accessioned2016-04-28T06:18:56Z
dc.date.available2016-04-28T06:18:56Z
dc.date.issued2003-09cs
dc.identifier.citationRadioengineering. 2003, vol. 12, č. 3, s. 51-55. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/58109
dc.description.abstractThe contribution describes the design, optimization and verification of the off-line single-trial movement classification system. Four types of movements are used for the classification: the right index finger extension vs. flexion as well as the right shoulder (proximal) vs. right index finger (distal) movement. The classification system utilizes hidden information stored in the characteristic shapes of human brain activity (EEG signal). The great variability of EEG potentials requires using of context information and hence the classifier based on Hidden Markov Models (HMM). The suitable parameterization, model structure as well as training and classification process are suggested on the base of spectral analysis results and experience with the speech recognition. The training and the classification are performed with the disjoint sets of EEG realizations. Classification experiments are performed with 10 randomly chosen sets of EEG realizations. The final average score of the distal/proximal movement classification is 80%; the standard deviation of classification results is 9%. The classification of the extension / flexion gives comparable results.en
dc.formattextcs
dc.format.extent51-55cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2003/03_03_51_55.pdfcs
dc.rightsCreative Commons Attribution 3.0 Unported Licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectHidden Markov modelsen
dc.subjectEEG classificationen
dc.subjectHTKen
dc.subjectBCI systemsen
dc.titleEEG Signal Classification: Introduction to the Problemen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
dc.coverage.issue3cs
dc.coverage.volume12cs
dc.rights.accessopenAccessen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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