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    EEG/MEG Source Reconstruction with Spatial-Temporal Two-Way Regularized Regression

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    Type
    Article
    Authors
    Tian, Tian Siva
    Huang, Jianhua Z.
    Shen, Haipeng
    Li, Zhimin
    KAUST Grant Number
    KUS-CI-016-04
    Date
    2013-07-11
    Online Publication Date
    2013-07-11
    Print Publication Date
    2013-10
    Permanent link to this record
    http://hdl.handle.net/10754/598047
    
    Metadata
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    Abstract
    In 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.
    Citation
    Tian 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.
    Sponsors
    This 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).
    Publisher
    Springer Nature
    Journal
    Neuroinformatics
    DOI
    10.1007/s12021-013-9193-2
    PubMed ID
    23842791
    ae974a485f413a2113503eed53cd6c53
    10.1007/s12021-013-9193-2
    Scopus Count
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