Modeling sparse connectivity between underlying brain sources for EEG/MEG

Stefan Haufe; Ryota Tomioka; Guido Nolte; Klaus Robert Müller; Motoaki Kawanabe

doi:10.1109/TBME.2010.2046325

Modeling sparse connectivity between underlying brain sources for EEG/MEG

Stefan Haufe^*
, Ryota Tomioka
, Guido Nolte
, Klaus Robert Müller
, Motoaki Kawanabe

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

90 Citations (Scopus)

Abstract

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.

Original language	English
Article number	5466024
Pages (from-to)	1954-1963
Number of pages	10
Journal	IEEE Transactions on Biomedical Engineering
Volume	57
Issue number	8
DOIs	https://doi.org/10.1109/TBME.2010.2046325
Publication status	Published - 2010 Aug
Externally published	Yes

Bibliographical note

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.

Keywords

Convolutive independent component analysis (ICA)
Granger Causality
electroencephalographic (EEG)
functional connectivity
magnetoencephalography (MEG)
source multivariate AR (MVAR) model

ASJC Scopus subject areas

Biomedical Engineering

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10.1109/TBME.2010.2046325

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title = "Modeling sparse connectivity between underlying brain sources for EEG/MEG",

abstract = "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.",

keywords = "Convolutive independent component analysis (ICA), Granger Causality, electroencephalographic (EEG), functional connectivity, magnetoencephalography (MEG), source multivariate AR (MVAR) model",

author = "Stefan Haufe and Ryota Tomioka and Guido Nolte and M{\"u}ller, \{Klaus Robert\} and Motoaki Kawanabe",

note = "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 f{\"u}r 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.",

year = "2010",

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doi = "10.1109/TBME.2010.2046325",

language = "English",

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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.

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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

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Modeling sparse connectivity between underlying brain sources for EEG/MEG

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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