Source Reconstruction Accuracy of MEG and EEG Bayesian Inversion Approaches

Research output: Contribution to journalArticle

Standard

Source Reconstruction Accuracy of MEG and EEG Bayesian Inversion Approaches. / Belardinelli, Paolo; Ortiz, Erick; Barnes, Gareth; Noppeney, Uta; Preissl, Hubert; Noppeney, Uta.

In: PLoS ONE, Vol. 7, No. 12, e51985, 25.12.2012.

Research output: Contribution to journalArticle

Harvard

Belardinelli, P, Ortiz, E, Barnes, G, Noppeney, U, Preissl, H & Noppeney, U 2012, 'Source Reconstruction Accuracy of MEG and EEG Bayesian Inversion Approaches', PLoS ONE, vol. 7, no. 12, e51985. https://doi.org/10.1371/journal.pone.0051985

APA

Belardinelli, P., Ortiz, E., Barnes, G., Noppeney, U., Preissl, H., & Noppeney, U. (2012). Source Reconstruction Accuracy of MEG and EEG Bayesian Inversion Approaches. PLoS ONE, 7(12), [e51985]. https://doi.org/10.1371/journal.pone.0051985

Vancouver

Author

Belardinelli, Paolo ; Ortiz, Erick ; Barnes, Gareth ; Noppeney, Uta ; Preissl, Hubert ; Noppeney, Uta. / Source Reconstruction Accuracy of MEG and EEG Bayesian Inversion Approaches. In: PLoS ONE. 2012 ; Vol. 7, No. 12.

Bibtex

@article{37fc6b4b0e7e40edabd86fac91f362e0,
title = "Source Reconstruction Accuracy of MEG and EEG Bayesian Inversion Approaches",
abstract = "Electro- and magnetoencephalography allow for non-invasive investigation of human brain activation and corresponding networks with high temporal resolution. Still, no correct network detection is possible without reliable source localization. In this paper, we examine four different source localization schemes under a common Variational Bayesian framework. A Bayesian approach to the Minimum Norm Model (MNM), an Empirical Bayesian Beamformer (EBB) and two iterative Bayesian schemes (Automatic Relevance Determination (ARD) and Greedy Search (GS)) are quantitatively compared. While EBB and MNM each use a single empirical prior, ARD and GS employ a library of anatomical priors that define possible source configurations. The localization performance was investigated as a function of (i) the number of sources (one vs. two vs. three), (ii) the signal to noise ratio (SNR; 5 levels) and (iii) the temporal correlation of source time courses (for the cases of two or three sources). We also tested whether the use of additional bilateral priors specifying source covariance for ARD and GS algorithms improved performance. Our results show that MNM proves effective only with single source configurations. EBB shows a spatial accuracy of few millimeters with high SNRs and low correlation between sources. In contrast, ARD and GS are more robust to noise and less affected by temporal correlations between sources. However, the spatial accuracy of ARD and GS is generally limited to the order of one centimeter. We found that the use of correlated covariance priors made no difference to ARD/GS performance.",
author = "Paolo Belardinelli and Erick Ortiz and Gareth Barnes and Uta Noppeney and Hubert Preissl and Uta Noppeney",
year = "2012",
month = dec
day = "25",
doi = "10.1371/journal.pone.0051985",
language = "English",
volume = "7",
journal = "PLoSONE",
issn = "1932-6203",
publisher = "Public Library of Science (PLOS)",
number = "12",

}

RIS

TY - JOUR

T1 - Source Reconstruction Accuracy of MEG and EEG Bayesian Inversion Approaches

AU - Belardinelli, Paolo

AU - Ortiz, Erick

AU - Barnes, Gareth

AU - Noppeney, Uta

AU - Preissl, Hubert

AU - Noppeney, Uta

PY - 2012/12/25

Y1 - 2012/12/25

N2 - Electro- and magnetoencephalography allow for non-invasive investigation of human brain activation and corresponding networks with high temporal resolution. Still, no correct network detection is possible without reliable source localization. In this paper, we examine four different source localization schemes under a common Variational Bayesian framework. A Bayesian approach to the Minimum Norm Model (MNM), an Empirical Bayesian Beamformer (EBB) and two iterative Bayesian schemes (Automatic Relevance Determination (ARD) and Greedy Search (GS)) are quantitatively compared. While EBB and MNM each use a single empirical prior, ARD and GS employ a library of anatomical priors that define possible source configurations. The localization performance was investigated as a function of (i) the number of sources (one vs. two vs. three), (ii) the signal to noise ratio (SNR; 5 levels) and (iii) the temporal correlation of source time courses (for the cases of two or three sources). We also tested whether the use of additional bilateral priors specifying source covariance for ARD and GS algorithms improved performance. Our results show that MNM proves effective only with single source configurations. EBB shows a spatial accuracy of few millimeters with high SNRs and low correlation between sources. In contrast, ARD and GS are more robust to noise and less affected by temporal correlations between sources. However, the spatial accuracy of ARD and GS is generally limited to the order of one centimeter. We found that the use of correlated covariance priors made no difference to ARD/GS performance.

AB - Electro- and magnetoencephalography allow for non-invasive investigation of human brain activation and corresponding networks with high temporal resolution. Still, no correct network detection is possible without reliable source localization. In this paper, we examine four different source localization schemes under a common Variational Bayesian framework. A Bayesian approach to the Minimum Norm Model (MNM), an Empirical Bayesian Beamformer (EBB) and two iterative Bayesian schemes (Automatic Relevance Determination (ARD) and Greedy Search (GS)) are quantitatively compared. While EBB and MNM each use a single empirical prior, ARD and GS employ a library of anatomical priors that define possible source configurations. The localization performance was investigated as a function of (i) the number of sources (one vs. two vs. three), (ii) the signal to noise ratio (SNR; 5 levels) and (iii) the temporal correlation of source time courses (for the cases of two or three sources). We also tested whether the use of additional bilateral priors specifying source covariance for ARD and GS algorithms improved performance. Our results show that MNM proves effective only with single source configurations. EBB shows a spatial accuracy of few millimeters with high SNRs and low correlation between sources. In contrast, ARD and GS are more robust to noise and less affected by temporal correlations between sources. However, the spatial accuracy of ARD and GS is generally limited to the order of one centimeter. We found that the use of correlated covariance priors made no difference to ARD/GS performance.

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U2 - 10.1371/journal.pone.0051985

DO - 10.1371/journal.pone.0051985

M3 - Article

C2 - 23284840

AN - SCOPUS:84871351411

VL - 7

JO - PLoSONE

JF - PLoSONE

SN - 1932-6203

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M1 - e51985

ER -