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Article: A prediction approach for multichannel EEG signals modeling using local wavelet SVM
Title | A prediction approach for multichannel EEG signals modeling using local wavelet SVM |
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Authors | |
Keywords | Electroencephalogram (EEG) signal Local prediction method Support vector machine (SVM) Wavelet kernel |
Issue Date | 2010 |
Publisher | IEEE |
Citation | Ieee Transactions On Instrumentation And Measurement, 2010, v. 59 n. 5, p. 1485-1492 How to Cite? |
Abstract | Accurate 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 Identifier | http://hdl.handle.net/10722/123834 |
ISSN | 2023 Impact Factor: 5.6 2023 SCImago Journal Rankings: 1.536 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
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dc.contributor.author | Shen, M | en_HK |
dc.contributor.author | Lin, L | en_HK |
dc.contributor.author | Chen, J | en_HK |
dc.contributor.author | Chang, CQ | en_HK |
dc.date.accessioned | 2010-09-30T06:42:46Z | - |
dc.date.available | 2010-09-30T06:42:46Z | - |
dc.date.issued | 2010 | en_HK |
dc.identifier.citation | Ieee Transactions On Instrumentation And Measurement, 2010, v. 59 n. 5, p. 1485-1492 | en_HK |
dc.identifier.issn | 0018-9456 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/123834 | - |
dc.description.abstract | Accurate 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.language | eng | - |
dc.publisher | IEEE | - |
dc.relation.ispartof | IEEE Transactions on Instrumentation and Measurement | en_HK |
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.subject | Electroencephalogram (EEG) signal | en_HK |
dc.subject | Local prediction method | en_HK |
dc.subject | Support vector machine (SVM) | en_HK |
dc.subject | Wavelet kernel | en_HK |
dc.title | A prediction approach for multichannel EEG signals modeling using local wavelet SVM | en_HK |
dc.type | Article | en_HK |
dc.identifier.openurl | http://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.email | Chang, CQ: cqchang@eee.hku.hk | en_HK |
dc.identifier.authority | Chang, CQ=rp00095 | en_HK |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1109/TIM.2010.2040905 | en_HK |
dc.identifier.scopus | eid_2-s2.0-77950916875 | en_HK |
dc.identifier.hkuros | 173585 | - |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-77950916875&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 59 | en_HK |
dc.identifier.issue | 5 | en_HK |
dc.identifier.spage | 1485 | en_HK |
dc.identifier.epage | 1492 | en_HK |
dc.identifier.isi | WOS:000276416100057 | - |
dc.publisher.place | United States | en_HK |
dc.identifier.scopusauthorid | Shen, M=7401466148 | en_HK |
dc.identifier.scopusauthorid | Lin, L=26025246000 | en_HK |
dc.identifier.scopusauthorid | Chen, J=34867847700 | en_HK |
dc.identifier.scopusauthorid | Chang, CQ=7407033052 | en_HK |
dc.identifier.issnl | 0018-9456 | - |