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- Publisher Website: 10.1109/TBME.2010.2089686
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Article: Local polynomial modeling of time-varying autoregressive models with application to time-frequency analysis of event-related EEG
Title | Local polynomial modeling of time-varying autoregressive models with application to time-frequency analysis of event-related EEG | ||||
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Authors | |||||
Keywords | Electroencephalogram event-related potential local polynomial modeling (LPM) time-frequency analysis (TFA) time-varying autoregressive (TVAR) model | ||||
Issue Date | 2011 | ||||
Publisher | IEEE. | ||||
Citation | Ieee Transactions On Biomedical Engineering, 2011, v. 58 n. 3 PART 1, p. 557-566 How to Cite? | ||||
Abstract | This 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 Identifier | http://hdl.handle.net/10722/143342 | ||||
ISSN | 2023 Impact Factor: 4.4 2023 SCImago Journal Rankings: 1.239 | ||||
ISI Accession Number ID |
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 Field | Value | Language |
---|---|---|
dc.contributor.author | Zhang, ZG | en_HK |
dc.contributor.author | Hung, YS | en_HK |
dc.contributor.author | Chan, SC | en_HK |
dc.date.accessioned | 2011-11-22T08:30:53Z | - |
dc.date.available | 2011-11-22T08:30:53Z | - |
dc.date.issued | 2011 | en_HK |
dc.identifier.citation | Ieee Transactions On Biomedical Engineering, 2011, v. 58 n. 3 PART 1, p. 557-566 | en_HK |
dc.identifier.issn | 0018-9294 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/143342 | - |
dc.description.abstract | This 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.language | eng | en_US |
dc.publisher | IEEE. | en_US |
dc.relation.ispartof | IEEE Transactions on Biomedical Engineering | en_HK |
dc.rights | ©2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Electroencephalogram | en_HK |
dc.subject | event-related potential | en_HK |
dc.subject | local polynomial modeling (LPM) | en_HK |
dc.subject | time-frequency analysis (TFA) | en_HK |
dc.subject | time-varying autoregressive (TVAR) model | en_HK |
dc.title | Local polynomial modeling of time-varying autoregressive models with application to time-frequency analysis of event-related EEG | en_HK |
dc.type | Article | en_HK |
dc.identifier.email | Zhang, ZG:zgzhang@eee.hku.hk | en_HK |
dc.identifier.email | Hung, YS:yshung@eee.hku.hk | en_HK |
dc.identifier.email | Chan, SC:scchan@eee.hku.hk | en_HK |
dc.identifier.authority | Zhang, ZG=rp01565 | en_HK |
dc.identifier.authority | Hung, YS=rp00220 | en_HK |
dc.identifier.authority | Chan, SC=rp00094 | en_HK |
dc.description.nature | published_or_final_version | en_US |
dc.identifier.doi | 10.1109/TBME.2010.2089686 | en_HK |
dc.identifier.pmid | 20977980 | - |
dc.identifier.scopus | eid_2-s2.0-79952179749 | en_HK |
dc.identifier.hkuros | 187898 | - |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-79952179749&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 58 | en_HK |
dc.identifier.issue | 3 PART 1 | en_HK |
dc.identifier.spage | 557 | en_HK |
dc.identifier.epage | 566 | en_HK |
dc.identifier.isi | WOS:000287661900011 | - |
dc.publisher.place | United States | en_HK |
dc.identifier.scopusauthorid | Zhang, ZG=8597618700 | en_HK |
dc.identifier.scopusauthorid | Hung, YS=8091656200 | en_HK |
dc.identifier.scopusauthorid | Chan, SC=13310287100 | en_HK |
dc.identifier.issnl | 0018-9294 | - |