Paper Info
Title
Application of multi-source minimum variance beamformers for reconstruction of correlated neural activity.
Abstract
Linearly constrained minimum variance beamformers are highly effective for analysis of weakly correlated brain activity, but their performance degrades when correlations become significant. Multiple constrained minimum variance (MCMV) beamformers are insensitive to source correlations but require a priori information about the source locations. Besides the question whether unbiased estimates of source positions and orientations can be obtained remained unanswered. In this work, we derive MCMV-based source localizers that can be applied to both induced and evoked brain activity. They may be regarded as a generalization of scalar minimum-variance beamformers for the case of multiple correlated sources. We show that for arbitrary noise covariance these beamformers provide simultaneous unbiased estimates of multiple source positions and orientations and remain bounded at singular points. We also propose an iterative search algorithm that makes it possible to find sources approximately without a priori assumptions about their locations and orientations. Simulations and analyses of real MEG data demonstrate that presented approach is superior to traditional single-source beamformers in situations where correlations between the sources are significant.
Year
DOI
Venue
2011
10.1016/j.neuroimage.2011.05.081
NeuroImage
Keywords
Field
DocType
Magnetoencephalography (MEG),Inverse solutions,Source analysis,Minimum variance beamformers,Correlated sources
Minimum-variance unbiased estimator,Mathematical optimization,Iterative search,A priori and a posteriori,Scalar (physics),Algorithm,Neural activity,Cognitive psychology,Multi-source,Mathematics,Bounded function,Covariance
Journal
Volume
Issue
ISSN
58
2
1053-8119
Citations 
PageRank 
References 
15
0.85
20
Authors
4
Name
Order
Citations
PageRank
Alexander Moiseev1222.71
John M Gaspar2150.85
Jennifer A Schneider3150.85
Anthony T Herdman4556.88