期刊
NEUROIMAGE
卷 44, 期 3, 页码 932-946出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2008.05.063
关键词
EEG; MEG; Inverse solver; l(1)-norm; Temporal basis functions; Second-order cone programming
资金
- NIH NAMIC [U54-EB005149]
- NIH NCRR mBIRN [U24-RR021382]
- NIH NINDS [R01-NS051826]
- NIH BRP [R01-E006385]
- NIH NCRR NAC [P41RR13218]
- NIH NCRR CFNT [P41-RR14075]
- NSF CAREER Award [0642971]
- U.S. DOE Award [DE-FG02-99ER62764]
- NSF graduate fellowship
We propose a novel l(1)l(2)-norm inverse solver for estimating the sources of EEG/MEG signals. Based on the standard l(1)-norm inverse solvers, this sparse distributed inverse solver integrates the l(1)-norm spatial model with a temporal model of the source signals in order to avoid unstable activation patterns and spiky reconstructed signals often produced by the currently used sparse solvers. The joint spatio-temporal model leads to a cost function with an l(1)l(2)-norm regularizer whose minimization can be reduced to a convex second-order cone programming (SOCP) problem and efficiently solved using the interior-point method. The efficient computation of the SOCP problem allows us to implement permutation tests for estimating statistical significance of the inverse solution. Validation with simulated and human MEG data shows that the proposed solver yields source time course estimates qualitatively similar to those obtained through dipole fitting, but without the need to specify the number of dipole sources in advance. Furthermore, the l(1)l(2)-norm solver achieves fewer false positives and a better representation of the source locations than the conventional l(2) minimum-norm estimates. (C) 2008 Elsevier Inc. All rights reserved.
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