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

TitleDetection of time-varying signals in the noise using normalised radial basis function neural network
Authors
KeywordsEvoked potentials (EPs)
Time-varying signal
Normalised RBF neural network
Issue Date2003
PublisherIEEE.
Citation
International Conference on Neural Networks and Signal Processing Proceedings, Nanjing, China, 14-17 December 2003, v. 1, p. 172-175 How to Cite?
AbstractEvoked 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.
Persistent Identifierhttp://hdl.handle.net/10722/46513
ISBN

 

DC FieldValueLanguage
dc.contributor.authorShen, MFen_HK
dc.contributor.authorZhang, YZen_HK
dc.contributor.authorTing, KHen_HK
dc.contributor.authorChan, FHYen_HK
dc.date.accessioned2007-10-30T06:51:39Z-
dc.date.available2007-10-30T06:51:39Z-
dc.date.issued2003en_HK
dc.identifier.citationInternational Conference on Neural Networks and Signal Processing Proceedings, Nanjing, China, 14-17 December 2003, v. 1, p. 172-175en_HK
dc.identifier.isbn0-7803-7702-8en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46513-
dc.description.abstractEvoked 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.en_HK
dc.format.extent240077 bytes-
dc.format.extent13817 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectEvoked potentials (EPs)en_HK
dc.subjectTime-varying signalen_HK
dc.subjectNormalised RBF neural networken_HK
dc.titleDetection of time-varying signals in the noise using normalised radial basis function neural networken_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0-7803-7702-8&volume=1&spage=172&epage=175&date=2003&atitle=Detection+of+time-varying+signals+in+the+noise+using+normalised+radial+basis+function+neural+networken_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICNNSP.2003.1279239en_HK
dc.identifier.scopuseid_2-s2.0-78650227387-
dc.identifier.hkuros95147-

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