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dc.contributor.authorDemirer, R. Murat
dc.contributor.authorÖzerdem, Mehmet S.
dc.contributor.authorBayrak, Coşkun
dc.date.accessioned2016-05-03T10:29:42Z
dc.date.available2016-05-03T10:29:42Z
dc.date.issued2009-03-30
dc.identifier.issn0165-0270
dc.identifier.urihttp://hdl.handle.net/11413/1262
dc.description.abstractThe study presented in this paper shows that electrocorticographic (ECoG) signals can be classified for making use of a human brain-computer interface (BCI) field. The results show that certain invariant phase transition features can be reliably used to classify two types of imagined movements accurately. Those are the left small-finger and tongue movements. Our approach consists of two main parts: channel selection based on Tsallis entropy in Hilbert domain and the nonlinear classification of motor imagery with support vector machines (SVMs). The new approach, based on Hilbert and statistical/entropy measurements, were combined with SVMs based on admissible kernels for classification purposes. The classification accuracy rates were 95% (264/278) and 73% (73/100) for training and testing sets, respectively. The results support the use of classification methods for ECoG-based BCIs. Published by Elsevier B.V.tr_TR
dc.language.isoen_UStr_TR
dc.publisherElsevier Science Bv, Po Box 211, 1000 Ae Amsterdam, Netherlandstr_TR
dc.relationJournal Of Neuroscience Methodstr_TR
dc.subjectBrain computer interfacetr_TR
dc.subjectECoGtr_TR
dc.subjectClassificationtr_TR
dc.subjectMulti-dimensional Hilbert Transformationtr_TR
dc.subjectSVMtr_TR
dc.subjectBci-Competition-IIItr_TR
dc.subjectEEGtr_TR
dc.subjectBeyin-bilgisayar Arayüzütr_TR
dc.subjectSınıflandırmatr_TR
dc.subjectÇok Boyutlu Hilbert Dönüşümütr_TR
dc.titleClassification of imaginary movements in ECoG with a hybrid approach based on Mmlti-dimensional hilbert-SVM solutiontr_TR
dc.typeArticletr_TR
dc.contributor.authorIDTR141152tr_TR
dc.identifier.wos000264013000029
dc.identifier.wos264013000029en


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