Classification of imaginary movements in ECoG with a hybrid approach based on multi-dimensional Hilbert-SVM solution

Demirer, R. MuratOzerdem, Mehmet SiracBayrak, Coskun2024-04-242024-04-2420090165-02701872-678Xhttps://doi.org/10.1016/j.jneumeth.2008.11.011https://hdl.handle.net/11468/15684The 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.eninfo:eu-repo/semantics/closedAccessBrain Computer InterfaceEcogClassificationMulti-Dimensional Hilbert TransformationSvmClassification of imaginary movements in ECoG with a hybrid approach based on multi-dimensional Hilbert-SVM solutionClassification of imaginary movements in ECoG with a hybrid approach based on multi-dimensional Hilbert-SVM solutionArticle1781214218WOS:0002640130000292-s2.0-594490834031908455610.1016/j.jneumeth.2008.11.011Q2Q3