Modeling Sparse Connectivity Between Underlying Brain Sources for EEG/MEG

被引：78

作者：

Haufe, Stefan ^{[1
]}

Tomioka, Ryota ^{[2
]}

Nolte, Guido ^{[3
]}

Mueller, Klaus-Robert ^{[1
]}

Kawanabe, Motoaki ^{[3
]}

机构：

[1] Berlin Inst Technol, D-10623 Berlin, Germany

[2] Univ Tokyo, Tokyo 1138654, Japan

[3] Fraunhofer Inst Comp Architecture & Software Tech, D-12489 Berlin, Germany

来源：

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | 2010年 / 57卷 / 08期

关键词：

Convolutive independent component analysis (ICA); electroencephalographic (EEG); functional connectivity; Granger Causality; magnetoencephalography (MEG); source multivariate AR (MVAR) model; BLIND SOURCE SEPARATION; FUNCTIONAL CONNECTIVITY; COMPONENT ANALYSIS; EEG DATA; SELECTION;

D O I：

10.1109/TBME.2010.2046325

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

We propose a novel technique to assess functional brain connectivity in electroencephalographic (EEG)/ magnetoencephalographic (MEG) signals. Our method, called sparsely connected sources analysis (SCSA), can overcome the problem of volume conduction by modeling neural data innovatively with the following ingredients: 1) the EEG/MEG is assumed to be a linear mixture of correlated sources following a multivariate autoregressive (MVAR) model; 2) the demixing is estimated jointly with the source MVAR parameters; and 3) overfitting is avoided by using the group lasso penalty. This approach allows us to extract the appropriate level of crosstalk between the extracted sources and, in this manner, we obtain a sparse data-driven model of functional connectivity. We demonstrate the usefulness of SCSA with simulated data and compare it to a number of existing algorithms with excellent results.

引用

页码：1954 / 1963

页数：10

共 40 条

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