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

Demirer R. M., Ozerdem M. S., Bayrak C.

JOURNAL OF NEUROSCIENCE METHODS, vol.178, no.1, pp.214-218, 2009 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 178 Issue: 1
  • Publication Date: 2009
  • Doi Number: 10.1016/j.jneumeth.2008.11.011
  • Journal Name: JOURNAL OF NEUROSCIENCE METHODS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.214-218
  • Istanbul Kültür University Affiliated: No

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.