Abstract
Electroencephalograph (EEG) based Brain-computer Inter- face (BCI) research provides a non-muscular communication to drive assistive devices using movement related signals, generated from the motor activation areas of the brain. The dimensions of the feature vector play an important role in BCI research, which not only increases the computational time but also reduces the accuracy of the classifiers. In this paper, we aim to reduce the redundant features of a feature vector obtained from motor imagery EEG signals to improve their corresponding classification. In this paper we have proposed a feature selection method based on Firefly Algorithm and Temporal Difference Q-Learning. Here, we have applied our proposed method to the wavelet transform features of a standard BCI competition dataset. Support Vector Machines have been employed to determine the fitness function of the proposed method and obtain the resultant classification accuracy. We have shown that the accuracy of the reduced feature are considerably higher than the original features. This paper also demonstrates the superiority of the new method to its competitor algorithms.
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
Tavella, M., Leeb, R., Rupp, R., Millan, J.R.: Towards Natural Non-invasive Hand Neuroprostheses for Daily Living. In: 32nd Annual Int. Conf. IEEE EMBS, pp. 126–129 (2010)
Muller-PutzGernot, R., Reinhold, S., Pfurtscheller, G., Neuper, C.: Temporal coding of brain patterns for direct limb control in humans. J. Fron. Neurosci. 4, 1–11 (2010)
Conradi, J., Blankertz, B., Tangermann, M., Kunzmann, V., Curio, G.: Brain-computer interfacing in tetraplegic patients with high spinal cord injury. Int. J. Bioelectromagnetism 11(2), 65–68 (2009)
Prasad, G., Herman, P., Coyle, D., McDonough, S., Crosbie, J.: Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study. J. Neuroeng. and Rehab. 7(1), 60–76 (2010)
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), 94–109 (2003)
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), 164–173 (2000)
Anderson, R.A., Musallam, S., Pesaran, B.: Selecting the signals for a brain-machine interface. Curr. Opin. Neurobiol. 14(6), 720–726 (2004)
Dornhege, G., Millan, J.R., Hinterberger, T., McFarland, D.J., Muller, K.R.: Toward Brain-Computer Interfacing. MIT Press, Massachusetts (2007)
Sanei, S., Chambers, J.A.: EEG Signal Processing. John Wiley & Sons, West Sussex (2007)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press (2006)
Rakotomamonjy, A., Guigue, V., Mallet, G., Alvarado, V.: Ensemble of svms for improving brain computer interface. In: Int. Conf. on Artificial Neural Networks (2005)
Lotte, F., Congedo, M., Lecuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for eeg-based brain-computer interfaces. J. Neural Eng. 4 (2007)
Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics 2, 433–459 (2010)
Comon, P.: Independent component analysis: a new concept. Signal Processing 36(3), 287–314 (1994)
Pudil, P., Novovicova, J., Kittler, J.: Floating search methods in feature selection. Pattern Recognition Letters 15, 1119–1125 (1994)
Hao, H., Liu, C.-L., Sako, H.: Comparison of Genetic Algortihm and Sequential Search Methods for Classifier Subset Selection. In: 7th Int. Conf Document Analysis & Recognition (ICDAR) (2003)
Leeb, R., Lee, F., Keinrath, C., Scherer, R., Bischof, H., Pfurtscheller, G.: Brain-computer communication: motivation, aim and impact of exploring a virtual apartment. IEEE Trans. Neural Sys. & Rehab. Engg. 15, 473–482 (2007)
Tamraz, J.C., Comair, Y.G.: Atlas of regional anatomy of the brain using MRI with functional correlates. Springer (2006)
Pfurtscheller, G., Lopes da Silva, F.H.: Event-related EEG/MEG synchronization and desynchronization: basic principles. J. Clin. Neurophysiology 110, 1842–1857 (1999)
Darvishi, S., Al-Ani, A.: Brain-computer interface analysis using continuous wavelet transform and adaptive neuro-fuzzy classifier. In: 29th Int. Annu. Conf. IEEE Eng. Med. Biol. Soc., pp. 3220–3223 (2007)
Gaing, Z.L., Huang, H.S.: Wavelet Based Neural Network For Power Disturbance Classification. IEEE Trans. Power Delivery 19(4), 1560–1568 (2004)
Kocaman, C., Ozdemir, M.: Comparison of Statistical Methods and Wavelet Energy Coefficients for Determining Two Common PQ Disturbances: Sag and Swell. In: Int. Conf. Electrical & Electronics Engg., ELECO 2009, pp. I-80–I-84 (2009)
Bhowmik, P., Rakshit, P., Konar, A., Nagar, A.K., Kim, E.: FA-TDQL: an adaptive memetic algorithm. In: Congress on Evolutionary Computation, pp. 1–8 (2012)
Yang, X.S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009)
Mitchell, T.: Machine Learning. McGraw Hill (1997)
Das, S., Abraham, A., Konar, A.: Automatic clustering using an improved differential evolution algorithm. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans 38(1) (2008)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Alpaydin, E.: Introduction to Machine Learning. MIT Press, Massachusetts (2009)
Neshatian, K., Zhang, M., Johnston, M.: Feature Construction and Dimension Reduction Using Genetic Programming. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol. 4830, pp. 160–170. Springer, Heidelberg (2007)
Yang, S.X., Hu, Y., Meng, M.Q.H.: A Knowledge Based GA for Path Planning of Multiple Mobile Robots in Dynamic Environments. In: IEEE Conf. Robotics, Automation & Mechatronics, pp. 1–6 (2006)
Storn, R., Price, K.V.: Differential Evolutiona simple and efficient heuristic for global optimization over continuous spaces. J. Global Optimization 11(4), 341–359 (1997)
Chakraborty, J., Konar, A.: A Distributed Multi Robot Path Planning Using Particle Swarm Optimization. In: 2nd Nat. Conf. Recent Trends in Information Systems, pp. 216–221 (2008)
Bhattacharjee, P., Rakshit, P., Goswami, I., Konar, A., Nagar, A.K.: Multi-robot path-planning using artificial bee colony optimization algorithm. In: Third World Congress on Nature & Biologically Inspired Computing, pp. 219–224 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Bhattacharyya, S., Rakshit, P., Konar, A., Tibarewala, D.N., Janarthanan, R. (2013). Feature Selection of Motor Imagery EEG Signals Using Firefly Temporal Difference Q-Learning and Support Vector Machine. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8298. Springer, Cham. https://doi.org/10.1007/978-3-319-03756-1_48
Download citation
DOI: https://doi.org/10.1007/978-3-319-03756-1_48
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03755-4
Online ISBN: 978-3-319-03756-1
eBook Packages: Computer ScienceComputer Science (R0)