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Conference Paper: Adaptive neural network filter for visual evoked potential estimation

TitleAdaptive neural network filter for visual evoked potential estimation
Authors
KeywordsAdaptive Neural Network Filter
Visual Evoked Potential
Ensemble Average
Linear Adaptive Filter
Issue Date1995
PublisherIEEE.
Citation
International Conference on Neural Networks Proceedings, Perth, WA, Australia, 27 November - 1 December 1995 , v. 5, p. 2293-2296 How to Cite?
AbstractThe authors describe a new approach to enhance the signal-to-noise-ratio (SNR) of visual evoked potential (VEP) based on an adaptive neural network filter. Neural networks are usually used in an nonadaptive way. The weights in the neural network are adjusted during training but remain constant in actual use. Here, the authors use an adaptive neural network filter with adaptation capabilities similar to those of the traditional linear adaptive filter and suitable training scheme is also examined. In contrast with linear adaptive filters, adaptive neural network filters possess nonlinear characteristics which can better match the nonlinear behaviour of evoked potential signals. Simulations employing VEP signals obtained experimentally confirm the superior performance of the adaptive neural network filter against traditional linear adaptive filter.
Persistent Identifierhttp://hdl.handle.net/10722/45873
ISSN

 

DC FieldValueLanguage
dc.contributor.authorFung, KSMen_HK
dc.contributor.authorLam, FKen_HK
dc.contributor.authorChan, FHYen_HK
dc.contributor.authorPoon, PWFen_HK
dc.contributor.authorLiu, JGen_HK
dc.date.accessioned2007-10-30T06:37:27Z-
dc.date.available2007-10-30T06:37:27Z-
dc.date.issued1995en_HK
dc.identifier.citationInternational Conference on Neural Networks Proceedings, Perth, WA, Australia, 27 November - 1 December 1995 , v. 5, p. 2293-2296en_HK
dc.identifier.issn1098-7576en_HK
dc.identifier.urihttp://hdl.handle.net/10722/45873-
dc.description.abstractThe authors describe a new approach to enhance the signal-to-noise-ratio (SNR) of visual evoked potential (VEP) based on an adaptive neural network filter. Neural networks are usually used in an nonadaptive way. The weights in the neural network are adjusted during training but remain constant in actual use. Here, the authors use an adaptive neural network filter with adaptation capabilities similar to those of the traditional linear adaptive filter and suitable training scheme is also examined. In contrast with linear adaptive filters, adaptive neural network filters possess nonlinear characteristics which can better match the nonlinear behaviour of evoked potential signals. Simulations employing VEP signals obtained experimentally confirm the superior performance of the adaptive neural network filter against traditional linear adaptive filter.en_HK
dc.format.extent349155 bytes-
dc.format.extent13817 bytes-
dc.format.extent8841 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rights©1995 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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectAdaptive Neural Network Filteren_HK
dc.subjectVisual Evoked Potentialen_HK
dc.subjectEnsemble Averageen_HK
dc.subjectLinear Adaptive Filteren_HK
dc.titleAdaptive neural network filter for visual evoked potential estimationen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1098-7576&volume=5&spage=2293&epage=2296&date=1995&atitle=Adaptive+neural+network+filter+for+visual+evoked+potential+estimationen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICNN.1995.488221en_HK
dc.identifier.hkuros12435-

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