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Conference Paper: Novel Approach for Time-Varying Bispectral Analysis of Non-Stationary EEG Signals

TitleNovel Approach for Time-Varying Bispectral Analysis of Non-Stationary EEG Signals
Authors
Issue Date2005
PublisherIEEE.
Citation
The 27th IEEE Engineering in Medicine and Biology Society Conference Proceedings, Shanghai, China, 1-4 September 2005, v. 1, p. 829-832 How to Cite?
Abstractnovel parametric method, based on the non-Gaussian AR model, is proposed for the partition of on-stationary EEG data into a finite set of third-order stationary segments. With the assumption of piecewise third-order stationarity of the signal, a series of parametric bispectral estimations of the non-stationary EEG data can be performed so as to describe the time-varying non-Gaussian nonlinear characteristics of the observed EEG signals. A practical method based on the fitness of third-order statistics of the signal by using the non-Gaussian AR model, together with an algorithm with CMI is presented. The experimental results with several simulations and clinical EEG signals have also been investigated and discussed. The results show successful performance of the proposed method in estimating the time-varying bispectral structures of the EEG signals.
Persistent Identifierhttp://hdl.handle.net/10722/45856
ISSN

 

DC FieldValueLanguage
dc.contributor.authorShen, Men_HK
dc.contributor.authorLiu, Yen_HK
dc.contributor.authorChan, FHYen_HK
dc.contributor.authorBeadle, PJen_HK
dc.date.accessioned2007-10-30T06:37:02Z-
dc.date.available2007-10-30T06:37:02Z-
dc.date.issued2005en_HK
dc.identifier.citationThe 27th IEEE Engineering in Medicine and Biology Society Conference Proceedings, Shanghai, China, 1-4 September 2005, v. 1, p. 829-832en_HK
dc.identifier.issn1557-170Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/45856-
dc.description.abstractnovel parametric method, based on the non-Gaussian AR model, is proposed for the partition of on-stationary EEG data into a finite set of third-order stationary segments. With the assumption of piecewise third-order stationarity of the signal, a series of parametric bispectral estimations of the non-stationary EEG data can be performed so as to describe the time-varying non-Gaussian nonlinear characteristics of the observed EEG signals. A practical method based on the fitness of third-order statistics of the signal by using the non-Gaussian AR model, together with an algorithm with CMI is presented. The experimental results with several simulations and clinical EEG signals have also been investigated and discussed. The results show successful performance of the proposed method in estimating the time-varying bispectral structures of the EEG signals.en_HK
dc.format.extent364089 bytes-
dc.format.extent13817 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2005 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.titleNovel Approach for Time-Varying Bispectral Analysis of Non-Stationary EEG Signalsen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1557-170X&volume=1&spage=829&epage=832&date=2005&atitle=Novel+Approach+for+Time-Varying+Bispectral+Analysis+of+Non-Stationary+EEG+Signalsen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.pmid17282312en_HK

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