Abstract
In this article we examine the performance of two well-known metaheuristic techniques (Genetic Algorithm and Simulating Annealing) for selecting the input features of a classifier in a BCI system. An important problem of the EEG-based BCI system consists in designing the EEG pattern classifier. The selection of the EEG channels used for building that learning predictor has impact in the classifier performance. We present results of both metaheuristic techniques on real data set when the classifier is a Bayesian predictor. We statistically compare that performances with a random selection of the EEG channels. According our empirical results our approach significantly increases the accuracy of the learning predictor.
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References
Frolov, A.A., Husek, D., Bobrov, P.: Comparison of four classification methods for brain computer interface. Neural Network World 21(2), 101–115 (2011)
Frolov, A.A., Husek, D., Bobrov, P., Mokienko, O., Tintera, J.: Sources of electrical brain activity most relevant to performance of brain-computer interface based on motor imagery. In: Brain-Computer Interface Systems - Recent Progress and Future Prospects, pp. 175–193. InTech (2013)
Bobrov, P., Frolov, A.A., Cantor, C., Fedulova, I., Bakhnyan, M., Zhavoronkov, A.: Brain-computer interface based on generation of visual images. PLOS ONE 6(6), 1–12 (2011)
Schröder, M., Bogdan, M., Hinterberger, T., Birbaumer, N.: Automated EEG feature selection for brain computer interfaces. In: First International IEEE EMBS Conference on Neural Engineering, pp. 626–629 (March 2003)
Peterson, D.A., Knight, J.N., Kirby, M.J., Anderson, C.W., Thaut, M.H.: Feature selection and blind source separation in an EEG-based brain-computer interface. EURASIP J. Appl. Signal Process. 2005, 3128–3140 (2005)
Bobrov, P.D., Korshakov, A.V., Roshchin, V.I., Frolov, A.A.: Bayesian Classifier for Brain-computer Interface B]ased on Mental Representation of Movements. Zh Vyssh Nerv Deiat Im I P Pavlova 62(1), 89–99 (2012)
Frolov, A.A., Husek, D., Bobrov, P., Korshakov, A., Chernikova, L., Konovalov, R., Mokienko, O.: Sources of EEG activity most relevant to performance of brain-computer interface based on motor imagery. Neural Network World 22(1), 21–37 (2012)
Institute of Higher Nervous Activity and Neurophysiology of RAS (IHNA & NPh RAS), Moscow, Russia, http://www.ihna.ru/en/
Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C++: The Art of Scientific Computing. Cambridge University Press (February 2002)
Reeves, C.R.: Genetic Algorithms for the Operations Research. INFORMS Journal of Computing 9(3), 231–250 (1997)
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Basterrech, S., Bobrov, P., Frolov, A., Húsek, D. (2015). Nature-Inspired Algorithms for Selecting EEG Sources for Motor Imagery Based BCI. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9120. Springer, Cham. https://doi.org/10.1007/978-3-319-19369-4_8
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DOI: https://doi.org/10.1007/978-3-319-19369-4_8
Publisher Name: Springer, Cham
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