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Article: Solving the MEG Inverse Problem: A Robust Two-Way Regularization Method

TitleSolving the MEG Inverse Problem: A Robust Two-Way Regularization Method
Authors
KeywordsBiomedical
Medical
Imaging
Robust design
Outliers
Issue Date2015
PublisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/tech/index.cfm?fuseaction=main
Citation
Technometrics, 2015, v. 57 n. 1, p. 123-137 How to Cite?
AbstractMagnetoencephalography (MEG) is a common noninvasive imaging modality for instantly measuring whole brain activities. One challenge in MEG data analysis is how to minimize the impact of the outliers that commonly exist in the images. This article proposes a robust two-way regularization approach to solve the important MEG inverse problem of reconstructing neuronal activities using the measured MEG signals. The proposed method is based on the distributed source model and produces a spatiotemporal solution for all the dipoles simultaneously. Unlike the traditional methods that use the squared error loss function, the proposed method uses a robust loss function, which improves the robustness of the results against outliers. To impose desirable spatial focality and temporal smoothness, the authors then penalize the robust loss through appropriate spatial-temporal two-way regularization. Furthermore, an alternating reweighted least-squares algorithm is developed to optimize the penalized model fitting criterion. Extensive simulation studies and a real-world MEG study clearly demonstrate the advantages of the proposed method over three non-robust methods.
Persistent Identifierhttp://hdl.handle.net/10722/232098
ISSN
2015 Impact Factor: 1.435
2015 SCImago Journal Rankings: 1.018

 

DC FieldValueLanguage
dc.contributor.authorTian, S-
dc.contributor.authorHuang, J-
dc.contributor.authorShen, H-
dc.date.accessioned2016-09-20T05:27:42Z-
dc.date.available2016-09-20T05:27:42Z-
dc.date.issued2015-
dc.identifier.citationTechnometrics, 2015, v. 57 n. 1, p. 123-137-
dc.identifier.issn0040-1706-
dc.identifier.urihttp://hdl.handle.net/10722/232098-
dc.description.abstractMagnetoencephalography (MEG) is a common noninvasive imaging modality for instantly measuring whole brain activities. One challenge in MEG data analysis is how to minimize the impact of the outliers that commonly exist in the images. This article proposes a robust two-way regularization approach to solve the important MEG inverse problem of reconstructing neuronal activities using the measured MEG signals. The proposed method is based on the distributed source model and produces a spatiotemporal solution for all the dipoles simultaneously. Unlike the traditional methods that use the squared error loss function, the proposed method uses a robust loss function, which improves the robustness of the results against outliers. To impose desirable spatial focality and temporal smoothness, the authors then penalize the robust loss through appropriate spatial-temporal two-way regularization. Furthermore, an alternating reweighted least-squares algorithm is developed to optimize the penalized model fitting criterion. Extensive simulation studies and a real-world MEG study clearly demonstrate the advantages of the proposed method over three non-robust methods.-
dc.languageeng-
dc.publisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/tech/index.cfm?fuseaction=main-
dc.relation.ispartofTechnometrics-
dc.subjectBiomedical-
dc.subjectMedical-
dc.subjectImaging-
dc.subjectRobust design-
dc.subjectOutliers-
dc.titleSolving the MEG Inverse Problem: A Robust Two-Way Regularization Method-
dc.typeArticle-
dc.identifier.emailShen, H: haipeng@hku.hk-
dc.identifier.authorityShen, H=rp02082-
dc.identifier.doi10.1080/00401706.2014.887594-
dc.identifier.hkuros263853-
dc.identifier.volume57-
dc.identifier.issue1-
dc.identifier.spage123-
dc.identifier.epage137-
dc.publisher.placeUnited States-

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