Classification of imaginary movements in ECoG with a hybrid approach based on multi-dimensional Hilbert-SVM solution
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Tarih
2009
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Brain Computer Interface, Ecog, Classification, Multi-Dimensional Hilbert Transformation, Svm
Kaynak
Journal of Neuroscience Methods
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
178
Sayı
1
