Performance Analysis of SVM, kNN and BPNN Classifiers for Motor Imagery

Performance Analysis of SVM, k-NN and BPNN Classifiers for Motor Imagery

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2014 by IJETT Journal
Volume-10 Number-1                          
Year of Publication : 2014
Authors : Indu Dokare , Naveeta Kant
  10.14445/22315381/IJETT-V10P205

Citation 

Indu Dokare , Naveeta Kant. "Performance Analysis of SVM, k-NN and BPNN Classifiers for Motor Imagery", International Journal of Engineering Trends and Technology (IJETT), V10(1),19-23 April 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract

This paper presents the results obtained by the experiments carried out in the project which aims to classify EEG signal for motor imagery into right hand movement and left hand movement in Brain Computer Interface (BCI) applications. In this project the feature extraction of the EEG signal has been carried out using Discrete Wavelet Transform (DWT). The wavelet coefficients as features has been classified using Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Backpropagation Neural Network (BPNN). The maximum classification accuracy obtained using SVM is 78.57%, using k-NN is 72% and using BPNN is 80%.

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Keywords
Brain Computer Interface (BCI), Motor Imagery, Electroencephalography (EEG), k-nearest Neighbor (k-NN), Support Vector Machine (SVM), Backpropagation Neural Network (BPNN).