Performance Analysis of SVM, k-NN and BPNN Classifiers for Motor Imagery
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2014 by IJETT Journal|
|Year of Publication : 2014|
|Authors : Indu Dokare , Naveeta Kant
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
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%.
 Wolpaw J R, Birbaumer N, D J, Dennis J. McFarlanda, Gert Pfurtschellere, Theresa M. Vaughan, “Brain- computer interfaces for communication and control,” Clinical Neurophysiology, vol. 113, 2002, pp. 767-791.
 Wolpaw J R, Birbaumer N, Heetderks W, et al. “Brain - computer interface technology : a review of the first international meeting,” IEEE Trans. Rehab. Eng, vol. 8, 2000, pp. 161-163.
 Jonathan R.Wolpaw,"Brain – computer interfaces as new brain output pathways", J. Physiology 579.3, 2007, pp 613–619.
 Saeid Sanei and J.A. Chambers, " EEG signal processing" A Textbook, John Wiley & Sons Ltd, 2007.
 Boyu Wang, Chi Man Wong, Feng Wan, Peng Un Mak, Pui In Mak, and Mang I Vai, "Comparison of Different Classification Methods for EEG-Based Brain Computer Interfaces: A Case Study”, IEEE, International Conference on Information and Automation, 2009.
 Ali Bashashati, Mehrdad Fatourechi, Rabab K Ward, Gary E Birch,”A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals", J. Neural Engg. 4, 2007, R32– R57.
 Ramaswamy Palaniappan,"Biological Signal Analysis" A textbbok, 2010, Ramaswamy Palaniappan and Ventus Publishing ApS.
 Robi Polikar, AWavelet Tutorial Part IV- Multiresolution Analysis: The Discrete Wavelet Transform, Rowan University.
 Boyu Wang, Chi Man Wong, Feng Wan, Peng Un Mak, Pui In Mak, and Mang I Vai, Comparison of Different Classification Methods for EEG-Based Brain Computer Interfaces: A Case Study”, IEEE, International Conference on Information and Automation, 2009, pp 1416-1421.
 M. A. Hassan, A.F. Ali, M. I. Eladawy, “Classification of the Imagination of the Left and Right hand Movements using EEG" IEEE, CIBEC`08, 2008.
 Andrea Kubler, Boris Kotchoubey, Jochen Kaiser, Wolpaw J. R., Birbaumer N., "Brain -Computer Communication- Unlocking the Locked In" Psychological Bulletin, American Psychological Association Inc., Vol. 127, No. 3, 2001, pp 358-375.
 Indu Dokare, Naveeta Kant, " A Study of Bain Computer Interface System", International Journal of Engineering Research & Technology (IJERT), Vol. 2 Issue 3, March - 2013.
 Dennis J. McFarland, Laurie A. Miner, Theresa M. Vaughan, and Jonathan R. Wolpaw, "Mu and Beta Rhythm Topographies During Motor Imagery and Actual Movements", Brain Topography, Volume 12. Number 3, 2000,pp 177-186.
 Indu Dokare, Naveeta Kant, "Classification of EEG Signal for Left and Right Hand Movement for Brain Computer Interface Applications", Proceedings, International Technological Conference - 2014, I-TechCON-2014, 03-04 Jan. 2014, pp 291-293.
 Dieter Devlaminck, Bart Wyns, Luc Boullart, Patrick Santensand Georges Otte, "Brain -Computer Interfaces: from theory to practice", ESANN` 2009 proceedings, European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, Bruges (Belgium), 22-24 April 2009, pp 416-424.
 Indu Dokare, Naveeta Kant, " EEG Signal Classification for Motor Imagery", International Conference and Workshop on Electronics & Telecommunication Engineering 28th Feb. & 1st March 2014, TCET, Mumbai, India, pp. 566 - 569
Brain Computer Interface (BCI), Motor Imagery, Electroencephalography (EEG), k-nearest Neighbor (k-NN), Support Vector Machine (SVM), Backpropagation Neural Network (BPNN).