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Article: A prediction approach for multichannel EEG signals modeling using local wavelet SVM

TitleA prediction approach for multichannel EEG signals modeling using local wavelet SVM
Authors
KeywordsElectroencephalogram (EEG) signal
Local prediction method
Support vector machine (SVM)
Wavelet kernel
Issue Date2010
PublisherIEEE
Citation
Ieee Transactions On Instrumentation And Measurement, 2010, v. 59 n. 5, p. 1485-1492 How to Cite?
AbstractAccurate modeling of the multichannel electroencephalogram (EEG) signal is an important issue in clinical practice. In this paper, we propose a new local spatiotemporal prediction method based on support vector machines (SVMs). Combining with the local prediction method, the sequential minimal optimization (SMO) training algorithm, and the wavelet kernel function, a local SMO-wavelet SVM (WSVM) prediction model is developed to enhance the efficiency, effectiveness, and universal approximation capability of the prediction model. Both the spatiotemporal modeling from the measured time series and the details of the nonlinear modeling procedures are discussed. Simulations and experimental results with real EEG signals show that the proposed method is suitable for real signal processing and is effective in modeling the local spatiotemporal dynamics. This method greatly increases the computational speed and more effectively captures the local information of the signal. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/123834
ISSN
2015 Impact Factor: 1.808
2015 SCImago Journal Rankings: 1.022
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorShen, Men_HK
dc.contributor.authorLin, Len_HK
dc.contributor.authorChen, Jen_HK
dc.contributor.authorChang, CQen_HK
dc.date.accessioned2010-09-30T06:42:46Z-
dc.date.available2010-09-30T06:42:46Z-
dc.date.issued2010en_HK
dc.identifier.citationIeee Transactions On Instrumentation And Measurement, 2010, v. 59 n. 5, p. 1485-1492en_HK
dc.identifier.issn0018-9456en_HK
dc.identifier.urihttp://hdl.handle.net/10722/123834-
dc.description.abstractAccurate modeling of the multichannel electroencephalogram (EEG) signal is an important issue in clinical practice. In this paper, we propose a new local spatiotemporal prediction method based on support vector machines (SVMs). Combining with the local prediction method, the sequential minimal optimization (SMO) training algorithm, and the wavelet kernel function, a local SMO-wavelet SVM (WSVM) prediction model is developed to enhance the efficiency, effectiveness, and universal approximation capability of the prediction model. Both the spatiotemporal modeling from the measured time series and the details of the nonlinear modeling procedures are discussed. Simulations and experimental results with real EEG signals show that the proposed method is suitable for real signal processing and is effective in modeling the local spatiotemporal dynamics. This method greatly increases the computational speed and more effectively captures the local information of the signal. © 2006 IEEE.en_HK
dc.languageeng-
dc.publisherIEEE-
dc.relation.ispartofIEEE Transactions on Instrumentation and Measurementen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2010 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.-
dc.subjectElectroencephalogram (EEG) signalen_HK
dc.subjectLocal prediction methoden_HK
dc.subjectSupport vector machine (SVM)en_HK
dc.subjectWavelet kernelen_HK
dc.titleA prediction approach for multichannel EEG signals modeling using local wavelet SVMen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0018-9456&volume=59&issue=5&spage=1485&epage=1492&date=2010&atitle=A+prediction+approach+for+multichannel+EEG+signals+modeling+using+local+wavelet+SVM-
dc.identifier.emailChang, CQ: cqchang@eee.hku.hken_HK
dc.identifier.authorityChang, CQ=rp00095en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TIM.2010.2040905en_HK
dc.identifier.scopuseid_2-s2.0-77950916875en_HK
dc.identifier.hkuros173585-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77950916875&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume59en_HK
dc.identifier.issue5en_HK
dc.identifier.spage1485en_HK
dc.identifier.epage1492en_HK
dc.identifier.isiWOS:000276416100057-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridShen, M=7401466148en_HK
dc.identifier.scopusauthoridLin, L=26025246000en_HK
dc.identifier.scopusauthoridChen, J=34867847700en_HK
dc.identifier.scopusauthoridChang, CQ=7407033052en_HK

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