TY - JOUR
T1 - Modeling sparse connectivity between underlying brain sources for EEG/MEG
AU - Haufe, Stefan
AU - Tomioka, Ryota
AU - Nolte, Guido
AU - Müller, Klaus Robert
AU - Kawanabe, Motoaki
N1 - Funding Information:
Manuscript received December 11, 2009; revised February 24, 2010; accepted March 13, 2010. Date of publication May 18, 2010; date of current version July 14, 2010. This work was supported in part by the Bundesministerium für Bildung und Forschung (BMBF) under Grant Fkz 01GQ0850 and in part by the European Information and Communication Technologies Programme under Project FP7-224631 and Project 216886. Asterisk indicates corresponding author.
PY - 2010/8
Y1 - 2010/8
N2 - 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.
AB - 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.
KW - Convolutive independent component analysis (ICA)
KW - Granger Causality
KW - electroencephalographic (EEG)
KW - functional connectivity
KW - magnetoencephalography (MEG)
KW - source multivariate AR (MVAR) model
UR - http://www.scopus.com/inward/record.url?scp=77954646035&partnerID=8YFLogxK
U2 - 10.1109/TBME.2010.2046325
DO - 10.1109/TBME.2010.2046325
M3 - Article
C2 - 20483681
AN - SCOPUS:77954646035
SN - 0018-9294
VL - 57
SP - 1954
EP - 1963
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 8
M1 - 5466024
ER -