Multiclass support vector machines for EEG-signals classification

Guler İ., Ubeyli E. D.

IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, vol.11, no.2, pp.117-126, 2007 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 11 Issue: 2
  • Publication Date: 2007
  • Doi Number: 10.1109/titb.2006.879600
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.117-126
  • Keywords: electroencephalogram (EEG) signals, Lyapunov exponents, multiclass support vector machine (SVM), probabilistic neural network (PNN), wavelet coefficients, EXTRACTION, EPILEPSY
  • Gazi University Affiliated: Yes


In this paper, we proposed the multiclass support vector machine (SVM) with the error-correcting output codes for the multiclass electroencephalogram (EEG) signals classification problem. The probabilistic neural network (PNN) and multilayer perceptron neural network were also tested and benchmarked for their performance on the classification of the EEG signals. Decision making was performed in two stages: feature extraction by computing the wavelet coefficients and the Lyapunov exponents and classification using the classifiers trained on the extracted features. The purpose was to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. Our research demonstrated that the wavelet coefficients and the Lyapunov exponents are the features which well represent the EEG signals and the multiclass SVM and PNN trained on these features achieved high classification accuracies.