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dc.contributor.authorDobiáš, Martin
dc.contributor.authorŠt'astný, Jakub
dc.date.accessioned2019-09-23T10:36:44Z-
dc.date.available2019-09-23T10:36:44Z-
dc.date.issued2015
dc.identifier.citation2015 International Conference on Applied Electronics: Pilsen, 8th – 9th September 2015, Czech Republic, p.19-22.en
dc.identifier.isbn978-80-261-0386-8 (Online)
dc.identifier.isbn978-80-261-0385-1 (Print)
dc.identifier.issn1803-7232 (Print)
dc.identifier.issn1805-9597 (Online)
dc.identifier.urihttp://hdl.handle.net/11025/35086
dc.format4 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherZápadočeská univerzita v Plznics
dc.rights© University of West Bohemiaen
dc.subjectElektroencefalografiecs
dc.subjectskryté Markovovy modelycs
dc.subjectplánování požadavků na materiálcs
dc.subjectelektrodycs
dc.subjectindexycs
dc.subjectčasově-frekvenční analýzacs
dc.subjectmodelování mozkucs
dc.titleClassifying direction of the right index finger movement from delta band activity using HMMen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThis contribution examines the usage of low frequency components (<; 5 Hz) in single trial EEG recordings obtained during right index finger movement for classification of reaching and grasping movements. These components contain delta band activity and Movement Related Potentials (MRPs) associated with the movements. Time-frequency development is used to classify the movements using Hidden Markov Model based classifier. It is shown that in some cases the utilization of these components can lead to a better classification score than the utilization of the previously used oscillatory activity in the μ and β bands, which are used as the reference here. The classification score has changed on average by -1.3% (-11.7% to +16.1%) compared to the referenced 5-40 Hz band. By choosing the newly examined band only for subjects where there is a benefit in it, a score of 90.9% was obtained (+2.9% improvement on reference itself). The examined frequency band is optimized for each subject as the inter-subject variability of EEG plays a role here.en
dc.subject.translatedelectroencephalographyen
dc.subject.translatedhidden Markov modelsen
dc.subject.translatedmaterials requirements planningen
dc.subject.translatedelectrodesen
dc.subject.translatedindexesen
dc.subject.translatedtime-frequency analysisen
dc.subject.translatedbrain modelingen
dc.type.statusPeer-revieweden
Appears in Collections:Applied Electronics 2015
Applied Electronics 2015

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