Classification of imaginary movements in ECoG with a hybrid approach based on multi-dimensional Hilbert-SVM solution
dc.contributor.author | Demirer, R. Murat | |
dc.contributor.author | Ozerdem, Mehmet Sirac | |
dc.contributor.author | Bayrak, Coskun | |
dc.date.accessioned | 2024-04-24T16:15:10Z | |
dc.date.available | 2024-04-24T16:15:10Z | |
dc.date.issued | 2009 | |
dc.department | Dicle Üniversitesi | en_US |
dc.description.abstract | The 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. | en_US |
dc.identifier.doi | 10.1016/j.jneumeth.2008.11.011 | |
dc.identifier.endpage | 218 | en_US |
dc.identifier.issn | 0165-0270 | |
dc.identifier.issn | 1872-678X | |
dc.identifier.issue | 1 | en_US |
dc.identifier.pmid | 19084556 | |
dc.identifier.scopus | 2-s2.0-59449083403 | |
dc.identifier.scopusquality | Q2 | |
dc.identifier.startpage | 214 | en_US |
dc.identifier.uri | https://doi.org/10.1016/j.jneumeth.2008.11.011 | |
dc.identifier.uri | https://hdl.handle.net/11468/15684 | |
dc.identifier.volume | 178 | en_US |
dc.identifier.wos | WOS:000264013000029 | |
dc.identifier.wosquality | Q3 | |
dc.indekslendigikaynak | Web of Science | |
dc.indekslendigikaynak | Scopus | |
dc.indekslendigikaynak | PubMed | |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.relation.ispartof | Journal of Neuroscience Methods | |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Brain Computer Interface | en_US |
dc.subject | Ecog | en_US |
dc.subject | Classification | en_US |
dc.subject | Multi-Dimensional Hilbert Transformation | en_US |
dc.subject | Svm | en_US |
dc.title | Classification of imaginary movements in ECoG with a hybrid approach based on multi-dimensional Hilbert-SVM solution | en_US |
dc.title | Classification of imaginary movements in ECoG with a hybrid approach based on multi-dimensional Hilbert-SVM solution | |
dc.type | Article | en_US |