Journal
NEUROIMAGE
Volume 49, Issue 1, Pages 641-655Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2009.06.083
Keywords
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Funding
- NIH [R01 DC006435, DC004855]
- NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE [R01NS066654] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON DEAFNESS AND OTHER COMMUNICATION DISORDERS [R01DC004855, R01DC010145, R01DC006435] Funding Source: NIH RePORTER
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The synchronous brain activity measured via MEG (or EEG) can be interpreted as arising from a collection (possibly large) of current dipoles or sources located throughout the cortex. Estimating the number, location, and time Course of these Sources remains a challenging task, one that is significantly compounded by the effects of source correlations and unknown orientations and by the presence of interference from spontaneous brain activity, sensor noise, and other artifacts. This paper derives an empirical Bayesian method for addressing each of these issues in a principled fashion. The resulting algorithm guarantees descent of a cost function uniquely designed to handle unknown orientations and arbitrary correlations. Robust interference suppression is also easily incorporated. In a restricted setting, the proposed method is shown to produce theoretically zero reconstruction error estimating multiple dipoles even in the presence of strong correlations and unknown orientations, unlike a variety of existing Bayesian localization methods or common signal processing techniques such as beamforming and sLORETA. Empirical results on both simulated and real data sets verify the efficacy of this approach. (C) 2009 Elsevier Inc. All rights reserved.
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