Abstract
Most electroencephalography (EEG) based brain-computer interface (BCI) systems perform brain signal recording using all possible electrodes. Recent studies have shown that the performance of a BCI system can be enhanced by removing noisy or task-irrelevant electrodes. This paper presents an automated channel selection algorithm using genetic algorithms (GA) and Bayesian linear discriminant analysis (BLDA) for a P300 based BCI. The proposed method was implemented on data set II obtained from the third BCI competition (2005). It was found that the proposed algorithm outperforms other existing channel selection method in terms of character recognition rate. The character recognition rate is maintained at approximately 90% when the number of channels used is reduced from 64 to 8. This confirms the validity of stochastic based GA as an alternative channel selection method. The selected channels indicate that the task-relevant features are concentrated mainly on the parietal and occipital lobe which agrees well with previous findings.
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References
Wolpaw, J.R., Birbaumer, N., Heetderks, W.J., McFarland, D.J., Peckham, P.H., Schalk, G.S., Donchin, E., Quotrano, L.A., Robinson, C.J., Vaughan, T.M.: Brain-Computer Interface Technology: A Review of the First International Meeting. IEEE Trans. Neural Syst. Rehabil. Eng. 8(2), 164–173 (2000)
Mak, J.N., Wolpaw, J.R.: Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospect. IEEE Reviews in Biomed. Eng. 2, 187–199 (2009)
Nicolas-Alonso, L.F., Gomez-Gil, J.: Brain Computer Interfaces, a Review. Sensors 12, 1211–1279 (2012)
Bashashati, A., Fatourechi, M., Ward, R.K., Birch, G.E.: A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. J. Neural Eng. 4, 32–57 (2007)
Mugler, E.M., Ruf, C.A., Halder, S., Bensch, M., Kubler, A.: Design and Implementation of a P300-Based Brain-Computer Interface for Controlling an Internet Browser. IEEE Trans. Neural Syst. Rehab. Eng. 15(6), 599–609 (2010)
Arvaneh, M., Guan, C., Ang, K.K., Quek, C.: Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI. IEEE Trans. Biomed. Eng. 58(6), 1865–1873 (2011)
Hoffmann, U., Yazdani, A., Vesin, J.M., Ebrahim, T.: Bayesian Feature Selection Applied in a P300 Brain-Computer Interface. In: Proc 16th European Signal Processing Conf. (2008)
Shahriari, Y., Erfanian, A.: A Mutual Information Based Channel Selection Scheme for P300-Based Brain Computer Interface. In: Proc. 5th Int. IEEE EMBS Conf. on Neural Eng., pp. 434–437 (2011)
Qi, H., Xu, M., Li, W., Yuan, D., Zhu, W., An, X., Ming, D., Wan, B., Wang, W.: Feature Selection Study of P300 Speller Using Support Vector Machine. In: Proc. IEEE Int. Conf. Robotics and Biomimetics, pp. 1331–1334 (2010)
Rakotomamonjy, A., Guigue, V.: BCI competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller. IEEE Trans. Biomed. Eng. 55, 1147–1154 (2008)
Krusienski, D.J., Sellers, E.W., McFarland, D.J., Vaughan, T.M., Wolpaw, J.R.: Toward enhanced P300 speller performance. J. Neurosci. Methods 167(1), 15–21 (2007)
Blankertz, B., Losch, F., Krauledat, M., Dornhege, G., Curio, G., Muller, K.R.: The Berlin brain-computer interface, Accurate performance from first session in BCI-naïve subjects. IEEE Trans. Biomed. Eng. 55(20), 2452–2462 (2008)
Cecotti, H., Graser, A.: Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interface. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 433–444 (2011)
Peng, H., Long, F., Ding, C.: Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8), 1226–1238 (2005)
Yang, J., Singh, H., Hines, E.L., Schaghecken, F., Iliescu, D.D., Leeson, M.M., Stocks, N.G.: Channel Selection and Classification of electroencephalogram signals: An artificial neural network and genetic algorithm-based approach. Art. Intell. Med. (2012)
Hoffmann, U., Vesin, J.M., Ebrahim, T., Diserens, K.: An efficient P300-based brain-computer interface for disabled subjects. J. Neurosci. Methods 167(1), 115–125 (2008)
Blankertz, B., Muller, K.R., Krusienski, D.J., Schalk, G., Wolpaw, J.R., Schlogl, A., Pfurtscheller, G., Millan, J.R., Schroder, M., Birbaumer, N.: The BCI competition III: Validating alternative approaches to actual BCI problems. IEEE Trans. Neural Syst. Rehabil. Eng. 14(2), 153–159 (2006)
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Kee, C.Y., Kuppan Chetty, R.M., Khoo, B.H., Ponnambalam, S.G. (2012). Genetic Algorithm and Bayesian Linear Discriminant Analysis Based Channel Selection Method for P300 BCI. In: Ponnambalam, S.G., Parkkinen, J., Ramanathan, K.C. (eds) Trends in Intelligent Robotics, Automation, and Manufacturing. IRAM 2012. Communications in Computer and Information Science, vol 330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35197-6_25
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DOI: https://doi.org/10.1007/978-3-642-35197-6_25
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