Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines

Comput Biol Med. 2008 Jan;38(1):14-22. doi: 10.1016/j.compbiomed.2007.06.002. Epub 2007 Jul 24.


A new approach based on the implementation of multiclass support vector machine (SVM) with the error correcting output codes (ECOC) is presented for classification of electroencephalogram (EEG) signals. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The aim of the study is classification of the EEG signals by the combination of eigenvector methods and multiclass SVM. The purpose is to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The present research demonstrated that the eigenvector methods are the features which well represent the EEG signals and the multiclass SVM trained on these features achieved high classification accuracies.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Artificial Intelligence*
  • Diagnosis, Computer-Assisted / methods
  • Electroencephalography / classification
  • Electroencephalography / methods*
  • Epilepsy / diagnosis
  • Epilepsy / physiopathology
  • Humans
  • Neural Networks, Computer
  • ROC Curve
  • Sensitivity and Specificity
  • Signal Processing, Computer-Assisted*