Adaptive filtering of evoked potentials with radial-basis-function neural network prefilter
| Autor: | Fhy Chan, F.K. Lam, Wei Qiu, P.W.F. Poon, K.S.A. Fung, Roger P. Hamernik |
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| Rok vydání: | 2002 |
| Předmět: |
Computer science
Speech recognition Central nervous system Biomedical Engineering Signal Background noise Signal-to-noise ratio medicine Animals Evoked potential Evoked Potentials Signal processing Artificial neural network business.industry Noise (signal processing) Signal Processing Computer-Assisted Pattern recognition Filter (signal processing) Adaptive filter Nonlinear system medicine.anatomical_structure Neural Networks Computer Rabbits Artificial intelligence business Algorithms |
| Zdroj: | IEEE Transactions on Biomedical Engineering. 49:225-232 |
| ISSN: | 0018-9294 |
| DOI: | 10.1109/10.983456 |
| Popis: | Evoked potentials (EPs) are time-varying signals typically buried in relatively large background noise. To extract the EP more effectively from noise, we had previously developed an approach using an adaptive signal enhancer (ASE) (Chen et al., 1995). ASE requires a proper reference input signal for its optimal performance. Ensemble- and moving window-averages were formerly used with good results. In this paper, we present a new method to provide even more effective reference inputs for the ASE. Specifically, a Gaussian radial basis function neural network (RBFNN) was used to preprocess raw EP signals before serving as the reference input. Since the RBFNN has built-in nonlinear activation functions that enable it to closely fit any function mapping, the output of RBFNN can effectively track the signal variations of EP. Results confirmed the superior performance of ASE with RBFNN over the previous method. |
| Databáze: | OpenAIRE |
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