EEG Signal Classification Based on Orthogonal Polynomials, Sparse Filter and SVM Classifier

Authors

  • Hayder S. Radeaf University of Baghdad
  • Mohammed Z. Al-Faiz College of Information and Communication Engineering, Al-Nahrain University

DOI:

https://doi.org/10.31987/ijict.6.3.234

Keywords:

EEG, Orthogonal Polynomials, Sparse Filter, SVM, Tchebichef, Krawtchouk

Abstract

This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it is obvious that the number of moments selected by the SP should exceed 30% of the overall EEG samples for accuracy to be over 90%.

Downloads

Published

2023-12-31

How to Cite

EEG Signal Classification Based on Orthogonal Polynomials, Sparse Filter and SVM Classifier. (2023). Iraqi Journal of Information and Communication Technology, 6(3), 26-35. https://doi.org/10.31987/ijict.6.3.234