NDLI: Detection of time-varying signals in the noise using normalised radial basis function neural network

Content Provider	IEEE Xplore Digital Library
Author	Minfen Shen Yuzheng Zhang Ting, K.H. Chan, F.H.Y.
Copyright Year	2003
Description	Author affiliation: Sci. Res. Center, Shantou Univ., Guangdong, China (Minfen Shen; Yuzheng Zhang)
Abstract	Evoked potentials (EPs) are the special signals that are non-stationary and corrupted by relatively large background noise. To extract the time-varying EP responses more correctly from the noise, a new method is proposed to investigate the problem of denoising the EP signals. The main objective is to estimate the amplitude and the latency without losing the individual properties of each epoch, which is meaningful to clinicians and recognition problems. A normalized radial basis function neural network (NRBFNN) was presented to process the raw EP signals for the purpose of canceling the background noise. The output of NRBFNN enables to effectively track the EPs' variations since the proposed basis functions covers the whole input space with the same degree. Simulations and experimental results confirmed the superior performance of NRBFNN over other methods.
Sponsorship	IEEE Circuits and Syst. Soc. (CASS) IEEE R10 CASS IEEE Beijing Section Chinese Neural Networks Council Circuits and Information Processing Committee, CASS-CIE Southeast Univ., China
Starting Page	172
Ending Page	175
File Size	240077
Page Count	4
File Format	PDF
ISBN	0780377028
DOI	10.1109/ICNNSP.2003.1279239
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-12-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Intelligent networks Neural networks Radial basis function networks Signal processing Background noise Kernel Signal detection Delay Biological neural networks Signal to noise ratio
Content Type	Text
Resource Type	Article

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