Abstract
Aiming at the topic of electroencephalogram (EEG) pattern recognition in brain computer interface (BCI), a classification method based on probabilistic neural network (PNN) with supervised learning was presented in this paper. It applied the recognition rate of training samples to the learning progress of network parameters, The learning vector quantization is employed to group training samples and the Genetic algorithm (GA) is used for training the network’s smoothing parameters and hidden central vector for determining hidden neurons. Utilizing the standard dataset I(a) of BCI Competition 2003 and comparing with other classification methods, the experiment results show that this way has the best performance of pattern recognition, and the classification accuracy can reach 93.8%, which improves over 5% compared with the best result (88.7%) of the competition. This technology provides an effective way to EEG classification in practical system of BCI.
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References
Wolpaw, J.R., Birbaumer, N., Heetderks, W.J.: Brain computer interface technology: a review of the first international meeting. IEEE Trans. Rehab. Eng. 8(2), 64–73 (2000)
Millán, J., Mouriño, J., Franzé, M., et al.: A local neural classifier for the recognition of EEG patterns associated to mental tasks. IEEE Trans. Neural Networks 13(3), 678–686 (2002)
Millán, J., Mouriño, J.: Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project. IEEE Trans. Neural Syst. Rehab. Eng. 11(2), 159–161 (2003)
Wolpaw, J.R., McFarland, D.J., Neat, G.W., et al.: An EEG-based brain–computer interface for cursor control. Electroenceph Clin. Neurophysiol. 78(3), 25–29 (1991)
Birbaumer, N., Ghanayim, N., Hinterberger, T., et al.: A spelling device for the paralyzed. Nature 398(5), 297–298 (1999)
Pfurtscheller, G., Neuper, C., Schlogl, A., et al.: Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. IEEE Trans. Rehab. Eng. 6(3), 316–325 (1998)
Matthias, K., Peter, M.: BCI competition 2003-Data set IIb: support vector machines for the P300 speller paradigm. IEEE Transactions on Biomedical Engineering 51(6), 1073–1076 (2004)
Deriche, M., Al-Ani, A.: A new algorithm for EEG feature classification using mutual information. In: Proceedings of the 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, Salt Lake City, pp. 1057–1060 (2001)
Petersa, B.O., Pfurtschellerb, G., Flyvbjerg, H.: Mining multi-channel EEG for its information content: an ANN-based method for a brain-computer interface. Neural Networks 11(2), 1429–1433 (1998)
Specht, D.F.: Probabilistic neural network. Neural Network 3(2), 109–118 (1998)
Rutkowski, L.: Adaptive probabilistic neural networks for pattern classification in time-varying environment. IEEE Trans. on Neural Network 15(4), 811–827 (2004)
Hoya, T.: On the capability of accommodating new classes within probabilistic neural networks. IEEE Trans. on Neural Network 14(2), 450–453 (2003)
Rudolph, G.: Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Networks 5(1), 96–101 (1994)
Brett, D.M., Justin, W., Seung, H.S.: BCI Competition 2003-Data Set Ia: Combining gamma-band power with slow cortical potentials to improve single-trial classification of electroencephalographic signals. IEEE Transactions on Biomedical Engineering 51(6), 1052–1056 (2004)
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Wu, T., Yang, B., Sun, H. (2010). EEG Classification Based on Artificial Neural Network in Brain Computer Interface. In: Li, K., Li, X., Ma, S., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Communications in Computer and Information Science, vol 97. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15853-7_19
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