Abstract
Brain-computer Interface (BCI) has widespread use in Neuro-rehabilitation engineering. Electroencephalograph (EEG) based BCI research aims to decode the various movement related data generated from the motor areas of the brain. One of the issues in BCI research is the presence of redundant data in the features of a given dataset, which not only increases the dimensions but also reduces the accuracy of the classifiers. In this paper, we aim to reduce the redundant features of a dataset to improve the accuracy of classification. For this, we have employed Artificial Bee Colony (ABC) cluster algorithm to reduce the features and have acquired their corresponding accuracy. It is seen that for a reduced features of 200, the highest accuracy of 64.29 %. The results in this paper validate our claim.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Nijholt Anton, Tan Desney, “Brain computer interfacing for intelligent systems,” IEEE intelligent systems, 2008.
Vaughan T.M., Heetderks W.J., Trejo L.J., Rymer W.Z., Weinrich M., Moore M.M., Kubler A., Dobkin B.H., Birbaumer N., Donchin E., Wolpaw E.W., Wolpaw J.R. “ Brain computer interface technology: A review of the second international meeting”, IEEE Trans. Neural Syst. Rehab. Eng. 11(2), June 2003, 94-109.
Wolpaw J.R., Birbaumer N., Heetderks W.J., McFarland D.J., Peckham P.H., Schalk G., Donchin E., Quatrano L.A., Robinson C.J., Vaughan T.M. “ Brain computer interface : A review of the first international meeting”, IEEE Trans. Rehabilitation Eng. 8(2), June 2000, 164-173.
Daly Janis J, Wolpaw Jonathan R, “Brain–computer interfaces in neurological rehabilitation”, Lancet Neurol 2008; 7: 1032–43.
Schwartz A.B., Cui X.T., Weber D.J., Moran D.W. “Brain Controlled Interfaces: Movement Restoration using Neural Prosthetics.” Neuron 52, October 2006, 205-220.
Anderson R.A., Musallam S., Pesaran B., “Selecting the signals for a brain-machine interface”, Curr Opin Neurobiol 14(6), December 2004, 720-726.
Pfurscheller G., Neuper C., “Motor imagery activates primary sensorimotor area in humans”, Neuroscience Letters 239, December 1997, 65-68.
Pfurscheller G., Neuper C., Schlogl A., Lugger K. “Separability of EEG signals recorded during right and left motor imagery using Adaptive Autoregressive Parameters.” IEEE transaction on rehabilitation engineering 6 (3), September 1998, 316-325.
S. Theodoridis and K. Koutroumbas, Pattern Recognition, 3rd ed. Academic Press, 2006.
A. Rakotomamonjy, V. Guigue, G. Mallet, and V. Alvarado, “Ensemble of svms for improving brain computer interface”, International Conference on Artificial Neural Networks, 2005, pp 300.
F Lotte, M Congedo, A Lecuyer, F Lamarche and B. Arnaldi, “A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces”, Journal of Neural Engineering 4, 2007.
H. Abdi and L.J. Williams, “Principal component analysis.”. Wiley Interdisciplinary Reviews: Computational Statistics, 2: 433–459, 2010.
P. C. Hansen, “The truncated SVD as a method for regularization”. BIT 27: 534–553, 1987.
Pierre Comon, “Independent Component Analysis: a new concept”, Signal Processing, 36(3):287–314, 1994.
Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
Basturk. B., Karaboga, D.: An artificial bee colony (ABC) algorithm for numeric function optimization. Proceedings of the IEEE Swarm Intelligence Symposium 2006, Indianapolis, Indiana, USA, 12-14 May 2006.
Chou, C. H., Su, M. C., Lai, E.: A new cluster validity measure and its application to image compression. Pattern Analysis Application, vol. 7, no. 2, pp. 205–220, Jul. 2004.
Halkidi, M., Vazirgiannis, M.: Clustering validity assessment: Finding the optimal partitioning of a data set. Proceedings of IEEE ICDM, San Jose, CA, 2001, pp. 187–194.
Das, S., Abraham, A., Konar, A.: Automatic clustering using an improved differential evolution algorithm. IEEE Transactions in Systems, Man and Cybernetics, Part-A, January, 2008.
BCI Competition III: http://www.bbci.de/competition/iii/.
Herman P., Prasad G., McGinnity T.M., Coyle D. “Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification.” IEEE Trans. Neural sys. Rehab eng. 16(4), August 2008, pp. 317-326.
D.G. Childers, ed., Modern Spectrum Analysis, New York: IEEE Press, 1978.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer India
About this paper
Cite this paper
Rakshit, P., Bhattacharyya, S., Konar, A., Khasnobish, A., Tibarewala, D.N., Janarthanan, R. (2013). Artificial Bee Colony Based Feature Selection for Motor Imagery EEG Data. In: Bansal, J., Singh, P., Deep, K., Pant, M., Nagar, A. (eds) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Advances in Intelligent Systems and Computing, vol 202. Springer, India. https://doi.org/10.1007/978-81-322-1041-2_11
Download citation
DOI: https://doi.org/10.1007/978-81-322-1041-2_11
Published:
Publisher Name: Springer, India
Print ISBN: 978-81-322-1040-5
Online ISBN: 978-81-322-1041-2
eBook Packages: EngineeringEngineering (R0)