Real-time data-reusing adaptive learning of a radial basis function network for tracking evoked potentials

@article{Qiu2006RealtimeDA,
  title={Real-time data-reusing adaptive learning of a radial basis function network for tracking evoked potentials},
  author={Wei Qiu and Chunqi Chang and Wenqing Liu and Paul W. F. Poon and Yong Hu and Francis K. Lam and Roger P. Hamernik and Gang Wei and Francis H. Y. Chan},
  journal={IEEE Transactions on Biomedical Engineering},
  year={2006},
  volume={53},
  pages={226-237}
}
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… CONTINUE READING
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Adaptive Filter Theory, 4th ed

  • S. Haykin
  • Upper Saddle River, NJ: Prentice-Hall,
  • 2002
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