Publication:
Classification of imaginary movements in ECoG with a hybrid approach based on Mmlti-dimensional hilbert-SVM solution

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Date

2009-03-30

Authors

Demirer, R. Murat
Özerdem, Mehmet S.
Bayrak, Coşkun

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Elsevier Science Bv, Po Box 211, 1000 Ae Amsterdam, Netherlands

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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.

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Keywords

Brain computer interface, ECoG, Classification, Multi-dimensional Hilbert Transformation, SVM, Bci-Competition-III, EEG, Beyin-bilgisayar Arayüzü, Sınıflandırma, Çok Boyutlu Hilbert Dönüşümü

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