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Article: Local polynomial modeling of time-varying autoregressive models with application to time-frequency analysis of event-related EEG

TitleLocal polynomial modeling of time-varying autoregressive models with application to time-frequency analysis of event-related EEG
Authors
KeywordsElectroencephalogram
event-related potential
local polynomial modeling (LPM)
time-frequency analysis (TFA)
time-varying autoregressive (TVAR) model
Issue Date2011
PublisherIEEE.
Citation
Ieee Transactions On Biomedical Engineering, 2011, v. 58 n. 3 PART 1, p. 557-566 How to Cite?
AbstractThis paper proposes a new local polynomial modeling (LPM) method for identification of time-varying autoregressive (TVAR) models and applies it to time-frequency analysis (TFA) of event-related electroencephalogram (ER-EEG). The LPM method models the TVAR coefficients locally by polynomials and estimates the polynomial coefficients using weighted least-squares with a window having a certain bandwidth. A data-driven variable bandwidth selection method is developed to determine the optimal bandwidth that minimizes the mean squared error. The resultant time-varying power spectral density estimation of the signal is capable of achieving both high time resolution and high frequency resolution in the time-frequency domain, making it a powerful TFA technique for nonstationary biomedical signals like ER-EEG. Experimental results on synthesized signals and real EEG data show that the LPM method can achieve a more accurate and complete time-frequency representation of the signal. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/143342
ISSN
2015 Impact Factor: 2.468
2015 SCImago Journal Rankings: 1.201
ISI Accession Number ID
Funding AgencyGrant Number
University of Hong Kong CRCG
Funding Information:

Manuscript received August 4, 2010; revised September 22, 2010; accepted October 15, 2010. Date of publication October 25, 2010; date of current version February 18, 2011. This work was supported by the University of Hong Kong CRCG Small Project Funding. Asterisk indicates corresponding author.

References

 

DC FieldValueLanguage
dc.contributor.authorZhang, ZGen_HK
dc.contributor.authorHung, YSen_HK
dc.contributor.authorChan, SCen_HK
dc.date.accessioned2011-11-22T08:30:53Z-
dc.date.available2011-11-22T08:30:53Z-
dc.date.issued2011en_HK
dc.identifier.citationIeee Transactions On Biomedical Engineering, 2011, v. 58 n. 3 PART 1, p. 557-566en_HK
dc.identifier.issn0018-9294en_HK
dc.identifier.urihttp://hdl.handle.net/10722/143342-
dc.description.abstractThis paper proposes a new local polynomial modeling (LPM) method for identification of time-varying autoregressive (TVAR) models and applies it to time-frequency analysis (TFA) of event-related electroencephalogram (ER-EEG). The LPM method models the TVAR coefficients locally by polynomials and estimates the polynomial coefficients using weighted least-squares with a window having a certain bandwidth. A data-driven variable bandwidth selection method is developed to determine the optimal bandwidth that minimizes the mean squared error. The resultant time-varying power spectral density estimation of the signal is capable of achieving both high time resolution and high frequency resolution in the time-frequency domain, making it a powerful TFA technique for nonstationary biomedical signals like ER-EEG. Experimental results on synthesized signals and real EEG data show that the LPM method can achieve a more accurate and complete time-frequency representation of the signal. © 2006 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE.en_US
dc.relation.ispartofIEEE Transactions on Biomedical Engineeringen_HK
dc.rightsIEEE Transactions on Biomedical Engineering. Copyright © IEEE.-
dc.rights©2011 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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectElectroencephalogramen_HK
dc.subjectevent-related potentialen_HK
dc.subjectlocal polynomial modeling (LPM)en_HK
dc.subjecttime-frequency analysis (TFA)en_HK
dc.subjecttime-varying autoregressive (TVAR) modelen_HK
dc.titleLocal polynomial modeling of time-varying autoregressive models with application to time-frequency analysis of event-related EEGen_HK
dc.typeArticleen_HK
dc.identifier.emailZhang, ZG:zgzhang@eee.hku.hken_HK
dc.identifier.emailHung, YS:yshung@eee.hku.hken_HK
dc.identifier.emailChan, SC:scchan@eee.hku.hken_HK
dc.identifier.authorityZhang, ZG=rp01565en_HK
dc.identifier.authorityHung, YS=rp00220en_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.description.naturepublished_or_final_versionen_US
dc.identifier.doi10.1109/TBME.2010.2089686en_HK
dc.identifier.pmid20977980-
dc.identifier.scopuseid_2-s2.0-79952179749en_HK
dc.identifier.hkuros187898-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79952179749&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume58en_HK
dc.identifier.issue3 PART 1en_HK
dc.identifier.spage557en_HK
dc.identifier.epage566en_HK
dc.identifier.isiWOS:000287661900011-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridZhang, ZG=8597618700en_HK
dc.identifier.scopusauthoridHung, YS=8091656200en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK

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