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

Rustu Murat DEMIRER  Yukio KOSUGI  Halil Ozcan GULCUR  

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

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Summary: 
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.