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Computational Intelligence and Neuroscience
Volume 2015 (2015), Article ID 945729, 11 pages
Research Article

Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based -Means Clustering

1Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, India
2M. Tech., Computer Technology, National Institute of Technology, Raipur 492001, India
3Electrical and Electronics Engineering, National Institute of Technology, Raipur 492001, India
4Birla Institute of Technology, Mesra, Ranchi 835215, India

Received 17 October 2014; Revised 20 March 2015; Accepted 21 March 2015

Academic Editor: Steven L. Bressler

Copyright © 2015 Suraj et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based -means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based -means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based -means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.