Novel Coupled Map Lattice Model for Prediction of EEG Signal

Authors:
Minfen Shen;Lanxin Lin;Guoliang Chang
Affiliations:
College of Engineering, Shantou University, Guangdong, China 515063;College of Engineering, Shantou University, Guangdong, China 515063;College of Engineering, Shantou University, Guangdong, China 515063
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Year:
2008

Citing 3
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Nonlinear prediction of chaotic signals using a normalised radial basis function network

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Signal detection using the radial basis function coupled map lattice

IEEE Transactions on Neural Networks

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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.