Performance Analysis of SVM, k-NN and BPNN Classifiers for Motor Imagery
Journal Title: INTERNATIONAL JOURNAL OF ENGINEERING TRENDS AND TECHNOLOGY - Year 2014, Vol 10, Issue 1
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%.
Authors and Affiliations
Indu Dokare , Naveeta Kant
Keywords
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