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dc.contributor.authorTian, Tian Siva
dc.contributor.authorHuang, Jianhua Z.
dc.contributor.authorShen, Haipeng
dc.contributor.authorLi, Zhimin
dc.date.accessioned2016-02-25T13:11:39Z
dc.date.available2016-02-25T13:11:39Z
dc.date.issued2013-07-11
dc.identifier.citationTian TS, Huang JZ, Shen H, Li Z (2013) EEG/MEG Source Reconstruction with Spatial-Temporal Two-Way Regularized Regression. Neuroinformatics 11: 477–493. Available: http://dx.doi.org/10.1007/s12021-013-9193-2.
dc.identifier.issn1539-2791
dc.identifier.issn1559-0089
dc.identifier.pmid23842791
dc.identifier.doi10.1007/s12021-013-9193-2
dc.identifier.urihttp://hdl.handle.net/10754/598047
dc.description.abstractIn this work, we propose a spatial-temporal two-way regularized regression method for reconstructing neural source signals from EEG/MEG time course measurements. The proposed method estimates the dipole locations and amplitudes simultaneously through minimizing a single penalized least squares criterion. The novelty of our methodology is the simultaneous consideration of three desirable properties of the reconstructed source signals, that is, spatial focality, spatial smoothness, and temporal smoothness. The desirable properties are achieved by using three separate penalty functions in the penalized regression framework. Specifically, we impose a roughness penalty in the temporal domain for temporal smoothness, and a sparsity-inducing penalty and a graph Laplacian penalty in the spatial domain for spatial focality and smoothness. We develop a computational efficient multilevel block coordinate descent algorithm to implement the method. Using a simulation study with several settings of different spatial complexity and two real MEG examples, we show that the proposed method outperforms existing methods that use only a subset of the three penalty functions. © 2013 Springer Science+Business Media New York.
dc.description.sponsorshipThis work is supported in part by NIDA (1 RC1 DA029425-01), NSF (DMS-09-07170, DMS-10-07618, CMMI-0800575, DMS-11-06912, DMS-12-08952, and DMS-12-08786), and King Abdullah University of Science and Technology (KUS-CI-016-04).
dc.publisherSpringer Nature
dc.subjectCoordinate descent
dc.subjectGraph Laplacian
dc.subjectInverse problem
dc.subjectMEG
dc.subjectRoughness penalization
dc.subjectSparsity
dc.titleEEG/MEG Source Reconstruction with Spatial-Temporal Two-Way Regularized Regression
dc.typeArticle
dc.identifier.journalNeuroinformatics
dc.contributor.institutionUniversity of Houston, Houston, United States
dc.contributor.institutionTexas A and M University, College Station, United States
dc.contributor.institutionThe University of North Carolina at Chapel Hill, Chapel Hill, United States
dc.contributor.institutionMedical College of Wisconsin, Milwaukee, United States
kaust.grant.numberKUS-CI-016-04
dc.date.published-online2013-07-11
dc.date.published-print2013-10


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