File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Adaptive filtering of evoked potentials with radial-basis-function neural network prefilter

TitleAdaptive filtering of evoked potentials with radial-basis-function neural network prefilter
Authors
Issue Date2002
PublisherIEEE.
Citation
IEEE Transactions on Biomedical Engineering, 2002, v. 49 n. 3, p. 225-232 How to Cite?
AbstractEvoked 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.
Persistent Identifierhttp://hdl.handle.net/10722/42902
ISSN
2015 Impact Factor: 2.468
2015 SCImago Journal Rankings: 1.201
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorQiu, Wen_HK
dc.contributor.authorFung, KSAen_HK
dc.contributor.authorChan, FHYen_HK
dc.contributor.authorLam, FKen_HK
dc.contributor.authorPoon, PWFen_HK
dc.contributor.authorHamernik, RPen_HK
dc.date.accessioned2007-03-23T04:34:21Z-
dc.date.available2007-03-23T04:34:21Z-
dc.date.issued2002en_HK
dc.identifier.citationIEEE Transactions on Biomedical Engineering, 2002, v. 49 n. 3, p. 225-232en_HK
dc.identifier.issn0018-9294en_HK
dc.identifier.urihttp://hdl.handle.net/10722/42902-
dc.description.abstractEvoked 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.en_HK
dc.format.extent189960 bytes-
dc.format.extent26624 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/msword-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2002 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.titleAdaptive filtering of evoked potentials with radial-basis-function neural network prefilteren_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0018-9294&volume=49&issue=3&spage=225&epage=232&date=2002&atitle=Adaptive+filtering+of+evoked+potentials+with+radial-basis-function+neural+network+prefilteren_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/10.983456en_HK
dc.identifier.pmid11878313-
dc.identifier.scopuseid_2-s2.0-0036173410-
dc.identifier.hkuros72151-
dc.identifier.isiWOS:000173849200005-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats