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Conference Paper: Local polynomial modelling of time-varying autoregressive processes and its application to the analysis of event-related electroencephalogram

TitleLocal polynomial modelling of time-varying autoregressive processes and its application to the analysis of event-related electroencephalogram
Authors
KeywordsBandwidth selections
Biomedical signal
Conventional methods
Data-driven
High resolution
Issue Date2010
PublisherIEEE.
Citation
The IEEE International Symposium on Circuits and Systems (ISCAS 2010), Paris, France, 30 May-2 June 2010. In Proceedings of ISCAS, 2010, p. 3124-3127 How to Cite?
AbstractThis paper proposes a new method for identification of time-varying autoregressive (TVAR) models based on local polynomial modeling (LPM) and applies it to investigate the dynamic spectral information of event-related electroencephalogram (EEG). The proposed method models the TVAR coefficients locally by polynomials and estimates those using least-squares estimation with a kernel having a certain bandwidth. A data-driven variable bandwidth selection method is developed to obtain the optimal bandwidth, which minimizes the mean squared error (MSE). Simulation results show that the LPM-based TVAR identification method outperforms conventional methods for different scenarios. The advantages of the LPM method make it a useful high-resolution timefrequency analysis (TFA) technique for nonstationary biomedical signals like EEG. Experimental results show that the LPM method can reveal more meaningful time-frequency characteristics than wavelet transform. ©2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/126129
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorZhang, ZGen_HK
dc.contributor.authorChan, SCen_HK
dc.contributor.authorHung, YSen_HK
dc.date.accessioned2010-10-31T12:11:22Z-
dc.date.available2010-10-31T12:11:22Z-
dc.date.issued2010en_HK
dc.identifier.citationThe IEEE International Symposium on Circuits and Systems (ISCAS 2010), Paris, France, 30 May-2 June 2010. In Proceedings of ISCAS, 2010, p. 3124-3127en_HK
dc.identifier.issn0271-4302-
dc.identifier.urihttp://hdl.handle.net/10722/126129-
dc.description.abstractThis paper proposes a new method for identification of time-varying autoregressive (TVAR) models based on local polynomial modeling (LPM) and applies it to investigate the dynamic spectral information of event-related electroencephalogram (EEG). The proposed method models the TVAR coefficients locally by polynomials and estimates those using least-squares estimation with a kernel having a certain bandwidth. A data-driven variable bandwidth selection method is developed to obtain the optimal bandwidth, which minimizes the mean squared error (MSE). Simulation results show that the LPM-based TVAR identification method outperforms conventional methods for different scenarios. The advantages of the LPM method make it a useful high-resolution timefrequency analysis (TFA) technique for nonstationary biomedical signals like EEG. Experimental results show that the LPM method can reveal more meaningful time-frequency characteristics than wavelet transform. ©2010 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE.-
dc.relation.ispartofProceedings of the IEEE International Symposium on Circuits and Systems, ISCAS 2010en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsIEEE International Symposium on Circuits and Systems Proceedings. Copyright © IEEE.-
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.subjectBandwidth selections-
dc.subjectBiomedical signal-
dc.subjectConventional methods-
dc.subjectData-driven-
dc.subjectHigh resolution-
dc.titleLocal polynomial modelling of time-varying autoregressive processes and its application to the analysis of event-related electroencephalogramen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0271-4302&volume=&spage=3124&epage=3127&date=2010&atitle=Local+polynomial+modelling+of+time-varying+autoregressive+processes+and+its+application+to+the+analysis+of+event-related+electroencephalogram-
dc.identifier.emailChan, SC:scchan@eee.hku.hken_HK
dc.identifier.emailHung, YS:yshung@eee.hku.hken_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.identifier.authorityHung, YS=rp00220en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ISCAS.2010.5537961en_HK
dc.identifier.scopuseid_2-s2.0-77955990253en_HK
dc.identifier.hkuros174343en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77955990253&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage3124en_HK
dc.identifier.epage3127en_HK
dc.description.otherThe IEEE International Symposium on Circuits and Systems (ISCAS 2010), Paris, France, 30 May-2 June 2010. In Proceedings of ISCAS, 2010, p. 3124-3127-
dc.identifier.scopusauthoridZhang, ZG=8407277900en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridHung, YS=8091656200en_HK

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