1. Real-Time Data-Reusing Adaptive Learning of a Radial Basis Function Network for Tracking Evoked Potentials.
- Author
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Wei Qiu, Chunqi Chang, Wenqing Liu, Poon, Paul W. F., Yong Hu, Lam, F. K., Hamernik, Roger P., Wei, Gang, and Chan, Francis H. Y.
- Subjects
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EVOKED potentials (Electrophysiology) , *NERVOUS system , *RADIAL basis functions , *ELECTROENCEPHALOGRAPHY , *ALGORITHMS , *ELECTROPHYSIOLOGY , *APPROXIMATION theory , *TRANSCRANIAL magnetic stimulation , *NEUROSCIENCES - Abstract
Tracking variations in both the latency and amplitude of evoked potential (EP) is important in quantifying properties of the nervous system. Adaptive filtering is a powerful tool for tracking such variations. In this paper, a data-reusing nonlinear adaptive filtering method, based on a radial basis function network (RBFN), is implemented to estimate EP. The RBFN consists of an input layer of source nodes, a single hidden layer of nonlinear processing units and an output layer of linear weights. It has built-in nonlinear activation functions that allow learning of function mappings. Moreover, it produces satisfactory estimates of signals against a background noise without a priori knowledge of the signal, provided that the signal and noise are independent. In clinical situations where EP responses change rapidly, the convergence rate of the algorithm becomes a critical factor. A carefully designed data-reusing RBFN can accelerate the convergence rate markedly and, thus, enhance its performance. Both theoretical analysis and simulation results support the improved performance of our new algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2006
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