Journal
NEUROIMAGE
Volume 60, Issue 1, Pages 305-323Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2011.12.027
Keywords
Inverse problem; Magnetoencephalography; Electroencephalography; Bayesian
Funding
- ARCS
- NIH [R21 NS076171, RO1 DC004855, DC006435, DC010145, NS067962, NS64060]
- NSF [BCS-0926196]
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In this paper, we present an extensive performance evaluation of a novel source localization algorithm, Champagne. It is derived in an empirical Bayesian framework that yields sparse solutions to the inverse problem. It is robust to correlated sources and learns the statistics of non-stimulus-evoked activity to suppress the effect of noise and interfering brain activity. We tested Champagne on both simulated and real M/EEG data. The source locations used for the simulated data were chosen to test the performance on challenging source configurations. In simulations, we found that Champagne outperforms the benchmark algorithms in terms of both the accuracy of the source localizations and the correct estimation of source time courses. We also demonstrate that Champagne is more robust to correlated brain activity present in real MEG data and is able to resolve many distinct and functionally relevant brain areas with real MEG and EEG data. (C) 2011 Elsevier Inc. All rights reserved.
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