Abstract：
:We propose a new MUSIC-like method, called 2q-ExSo-MUSIC (q ≥ 1). This method is an extension of the 2q-MUSIC (q ≥ 1) approach for solving the EEG/MEG inverse problem, when spatially-extended neocortical sources ("ExSo") are considered. It introduces a novel ExSo-MUSIC principle. The novelty is two-fold: i) the parameterization of the spatial source distribution that leads to an appropriate metric in the context of distributed brain sources and ii) the introduction of an original, efficient and low-cost way of optimizing this metric. In 2q-ExSo-MUSIC, the possible use of higher order statistics (q ≥ 2) offers a better robustness with respect to Gaussian noise of unknown spatial coherence and modeling errors. As a result we reduced the penalizing effects of both the background cerebral activity that can be seen as a Gaussian and spatially correlated noise, and the modeling errors induced by the non-exact resolution of the forward problem. Computer results on simulated EEG signals obtained with physiologically-relevant models of both the sources and the volume conductor show a highly increased performance of our 2q-ExSo-MUSIC method as compared to the classical 2q-MUSIC algorithms.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Birot G,Albera L,Wendling F,Merlet Idoi
10.1016/j.neuroimage.2011.01.054subject
Has Abstractpub_date
2011-05-01 00:00:00pages
102-13issue
1eissn
1053-8119issn
1095-9572pii
S1053-8119(11)00090-5journal_volume
56pub_type
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