論文

査読有り
2010年8月

Modeling Sparse Connectivity Between Underlying Brain Sources for EEG/MEG

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
  • Stefan Haufe
  • ,
  • Ryota Tomioka
  • ,
  • Guido Nolte
  • ,
  • Klaus-Robert Mueller
  • ,
  • Motoaki Kawanabe

57
8
開始ページ
1954
終了ページ
1963
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1109/TBME.2010.2046325
出版者・発行元
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

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.

Web of Science ® 被引用回数 : 75

リンク情報
DOI
https://doi.org/10.1109/TBME.2010.2046325
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000282000900015&DestApp=WOS_CPL
URL
http://dblp.uni-trier.de/db/journals/tbe/tbe57.html#journals/tbe/HaufeTNMK10
ID情報
  • DOI : 10.1109/TBME.2010.2046325
  • ISSN : 0018-9294
  • DBLP ID : journals/tbe/HaufeTNMK10
  • Web of Science ID : WOS:000282000900015

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