Abstract
Electroencephalogram (EEG) is widely regarded as chaotic signal. Modeling and prediction of EEG signals is important for many applications. The method using support vectors machine (SVM) based on the structure risk minimization provides us an effective way of learning machine. The performance of SVM is much better than the traditional learning machine. Now the SVM is used in classification and regression. But solving the quadratic programming problem for training SVM becomes a bottle-neck of using SVM because of the long time of SVM training. In this paper, a local-SVM method is proposed for predicting the signals. The local method is presented for improving the speed of the prediction of EEG signals. The simulation results show that the training of the local-SVM obtains a good behavior. In addition, the local SVM method significantly improves the prediction precision.
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References
Lasemidis, L.D., Sackellares, J.C.: Chaos Theory and Eilepsy. The Neuroscientist 2, 118–126 (1996)
Lin, X.B., Qiu, T.S.: EEG Signal Analysis and Processing based on Prediction of Epileptic Seizures and Research Progress. Biomedical Engineering Foreign Medica1 Sciences 27, 9–12 (2004) (in Chinese)
Takens, F.: Dynamical Systems and Turbulence. Spring Verlag, Berlin (1981)
Kaneko, K.: Pattern Dynamic in Spatiotemporal Chaos. Phisica D 34, 1–44 (1989)
Leung, H., Haykin, S.: Detection and Estimation using an Adaptive Rational Function Filter. IEEE Trans. Signal Processing 42(11), 3366–3376 (1994)
Leung, H.: Nonlinear Clutter Cancellation and Detection using a Memory-based Predictor. IEEE Trans. Aerosp. Electron. System. 32(4), 1249–1256 (1996)
Lo, J.T.: Synthetic Approach to Optimal Filtering. IEEE Trans. Neural Network. 5(5), 803–811 (1994)
Vapink, V.P.: The Nature of Statistical Learning Theory. Springer, NewYork (1995)
Smola, A.J., Scholkopf, B.: A Tutorial on Support Vector Regression. Statistics and Computing 14, 199–222 (1998)
Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Zhang, J.S., Dang, J.J., Li, H.C.: Spatiotemporal Chaos Sequence Prediction using Local Support Vector Machine. Acra Physica Sinca 56, 67–77 (2007) (in Chinese)
Colin, C.: Algorithmic Approaches to Training Support Vector Machines: A Survey. In: Proceedings of ESANN 2000, pp. 27–36. D-Facto Publications, Belgium (2000)
Joachims, T.: Making large Scale SVM Learning Practical. In: Advances in Kernel Methods: Upport Vector Machines. MIT Press, Cambridge (1998)
Platt, J.C.: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In: Advances in Kernel Methods: Support Vector Learning, pp. 185–208. MIT Press, Cambridge (1999)
Keerthi, S., Shevade, S., Bhattcharyya, C., et al.: Improvements to Platt’s SMO Algorithm for SVM Classifier Design. Neural Computation 13(3), 637–649 (2001)
Suykens, J.A.K., Vandewalle, J.: Least Squares Support Vector Machine Classifiers. Neural Processing Letters 9, 293–300 (1999)
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Sun, L., Lin, L., Lin, C. (2009). Modeling and Prediction of Nonlinear EEG Signal Using Local SVM Method. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_52
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DOI: https://doi.org/10.1007/978-3-642-03040-6_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03039-0
Online ISBN: 978-3-642-03040-6
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