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Conference Paper: A Least Across-segment Variance (LASV) Method for the Correction of EEG-fMRI Desynchronization

TitleA Least Across-segment Variance (LASV) Method for the Correction of EEG-fMRI Desynchronization
Authors
Issue Date2017
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001963
Citation
Proceedings of the 8th International IEEE EMBS Conference On Neural Engineering (NER’17), Shanghai, China, 25-28 May 2017 How to Cite?
AbstractSimultaneous collection of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) is a promising neuroimaging technique, which can provide high resolution in both spatial and temporal domain. Because EEG recorded in MRI scanners is heavily contaminated with gradient artefact (GA), removal of GA from EEG is a crucial step in EEG-fMRI data analysis. To date, the most efficient methods to remove GA are the average artefact subtraction (AAS) method and its extensions. However, these methods assume perfect synchronization between EEG and fMRI recording, which could be violated in practice. In this paper, a least across-segment variance (LASV) method is proposed for correcting EEG-fMRI desynchronization. Simulation and real data tests were conducted to check the performance of LASV method. The results suggested that the LASV method is able to efficiently correct EEG-fMRI desynchronization in both synthetic and real data, providing a powerful tool for improving the performance of GA removal for desynchronized EEG-fMRI data.
Persistent Identifierhttp://hdl.handle.net/10722/249524

 

DC FieldValueLanguage
dc.contributor.authorTan, A-
dc.contributor.authorTu, Y-
dc.contributor.authorFu, Z-
dc.contributor.authorHuang, G-
dc.contributor.authorHung, YS-
dc.contributor.authorZhang, Z-
dc.date.accessioned2017-11-21T03:03:27Z-
dc.date.available2017-11-21T03:03:27Z-
dc.date.issued2017-
dc.identifier.citationProceedings of the 8th International IEEE EMBS Conference On Neural Engineering (NER’17), Shanghai, China, 25-28 May 2017-
dc.identifier.urihttp://hdl.handle.net/10722/249524-
dc.description.abstractSimultaneous collection of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) is a promising neuroimaging technique, which can provide high resolution in both spatial and temporal domain. Because EEG recorded in MRI scanners is heavily contaminated with gradient artefact (GA), removal of GA from EEG is a crucial step in EEG-fMRI data analysis. To date, the most efficient methods to remove GA are the average artefact subtraction (AAS) method and its extensions. However, these methods assume perfect synchronization between EEG and fMRI recording, which could be violated in practice. In this paper, a least across-segment variance (LASV) method is proposed for correcting EEG-fMRI desynchronization. Simulation and real data tests were conducted to check the performance of LASV method. The results suggested that the LASV method is able to efficiently correct EEG-fMRI desynchronization in both synthetic and real data, providing a powerful tool for improving the performance of GA removal for desynchronized EEG-fMRI data.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001963-
dc.relation.ispartofInternational IEEE/EMBS Conference on Neural Engineering (CNE)-
dc.rightsInternational IEEE/EMBS Conference on Neural Engineering (CNE). Copyright © IEEE.-
dc.rights©2017 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.titleA Least Across-segment Variance (LASV) Method for the Correction of EEG-fMRI Desynchronization-
dc.typeConference_Paper-
dc.identifier.emailHung, YS: yshung@hkucc.hku.hk-
dc.identifier.authorityHung, YS=rp00220-
dc.identifier.doi10.1109/NER.2017.8008278-
dc.identifier.hkuros282957-
dc.publisher.placeUnited States-

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