The Determination of the Evoked Potential Generating Mechanism Based on Radial Basis Neural Network Model

Rustu Murat DEMIRER  Yukio KOSUGI  Halil Ozcan GULCUR  

IEICE TRANSACTIONS on Information and Systems   Vol.E83-D   No.9   pp.1819-1823
Publication Date: 2000/09/25
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Biocybernetics, Neurocomputing
visual evoked potentials,  radial basis functions,  nonlinear system identification,  auto-regressive moving average,  neural networks,  

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This paper investigates the modeling of non-linearity on the generation of the single trial evoked potential signal (s-EP) by means of using a mixed radial basis function neural network (M-RBFN). The more emphasis is put on the contribution of spontaneous EEG term to s-EP signal. The method is based on a nonlinear M-RBFN neural network model that is trained simultaneously with the different segments of EEG/EP data. Then, the output of the trained model (estimator) is a both fitted and reduced (optimized) nonlinear model and then provide a global representation of the passage dynamics between spontaneous brain activity and poststimulus periods. The performance of the proposed neural network method is evaluated using a realistic simulation and applied to a real EEG/EP measurement.