Abstract
Considering that electroencephalogram(EEG) signal is a typical chaotic signal induced from the nonlinear spatial temporal dynamic system, in this paper, we propose a new spatial temporal model combined couple map lattices(CML) with normalized radial basis function(NRBF), namely CML-NRBF model. NRBF neural network is employed to reconstruct the nonlinear map to obtain a more robust model with low sensitive for the selection of the basis function parameters. In particular, genetic algorithm (GA) is used to search for the optimal parameters of the proposed model, including the spatial coupling coefficients and the centers of NRBF network. The effectiveness of the proposed model is illustrated in terms of several experiments with real EEG by comparing the prediction and detection results of the presented model with the common RBF network.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Babloyantz, A., Destexhe, A.: Low-dimensional Chaos in an Instance of Epilepsy. Proceeding of the National Academy of Sciences of the United States of American 83, 3513–3517 (1986)
Dudkin, A.O., Sbitnev, V.I.: Coupled Map Lattice Simulation of Epileptogenesis in Hippocampal Slice. Biological Cybernetics 78, 479–486 (1998)
Leung, H., Hennessey, G., Drosopoulos, A.: Signal Detection Using the Radial Basis Function Coupled Map Lattice. IEEE Transaction on Neural Networks 11, 1133–1151 (2000)
Oketani, N., Ushio, T.: Chaotic Synchronization of Globally Coupled Maps with an Application in Communication. International Journal of Bifurcation and Chaos 6, 2145–2152 (1996)
Soong, A.C.K.: Evidence of Chaotic Dynamics Underlying the Human Alpha-Rhythm Electroencephalogram. Biological Cybernetics 62, 55–62 (1989)
Matthew, B.K., Reggie, B., Henry, D.I.A.: Determining Embedding Dimension for Phase-space Reconstruction using a Geometrical Construction. Physical Review A 45, 3403–3411 (1992)
Matthew, B.K., Henry, D.I.A.: False Neighbors and False Strands: A Reliable Minimum Embedding Dimension Algorithm. Physical Review E 66, 026209 (2002)
Cao, L.Y.: Practical Method for Determining the Minimum Embedding Dimension of a Scalar Time Series. Physica D 110, 43–50 (1997)
Cowper, M.R., Mulgrew, B.C., Unsworth, P.: Nonliear Prediction of Chaotic Signal using a Normalized Radial Basis Function Network. Signal Processing 82, 775–789 (2002)
Lorenz, E.N.: Dimension0 of Weather and Climate Attractors. Nature 353, 241–244 (1991)
Lachaux, J.P.: Spatial Extension of Brain Activity Fools the Single-Channel Reconstruction of EEG Dynamics. Human Brain Mapping 5, 26–47 (1997)
Haralambos, S., Alex, A., Stefanos, M., George, B.: A New Algorithm for Developing Dynamics Radial Basis Function Neural Network Models based on Genetic Algorithms. Computers and Chemical Engineering 28, 209–217 (2004)
Nan, X., Leung, H.: Reconstruction of Piecewise Chaotic Dynamics Using a Genetic Algorithm Multiple Model Approach. IEEE Transaction on Circuits and Systems 51, 1210–1222 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shen, M., Lin, L., Chang, G. (2008). Novel Coupled Map Lattice Model for Prediction of EEG Signal. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_39
Download citation
DOI: https://doi.org/10.1007/978-3-540-87732-5_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87731-8
Online ISBN: 978-3-540-87732-5
eBook Packages: Computer ScienceComputer Science (R0)