Title
Non-Gaussian probabilistic MEG source localisation based on kernel density estimation.
Abstract
There is strong evidence to suggest that data recorded from magnetoencephalography (MEG) follows a non-Gaussian distribution. However, existing standard methods for source localisation model the data using only second order statistics, and therefore use the inherent assumption of a Gaussian distribution. In this paper, we present a new general method for non-Gaussian source estimation of stationary signals for localising brain activity from MEG data. By providing a Bayesian formulation for MEG source localisation, we show that the source probability density function (pdf), which is not necessarily Gaussian, can be estimated using multivariate kernel density estimators. In the case of Gaussian data, the solution of the method is equivalent to that of widely used linearly constrained minimum variance (LCMV) beamformer. The method is also extended to handle data with highly correlated sources using the marginal distribution of the estimated joint distribution, which, in the case of Gaussian measurements, corresponds to the null-beamformer. The proposed non-Gaussian source localisation approach is shown to give better spatial estimates than the LCMV beamformer, both in simulations incorporating non-Gaussian signals, and in real MEG measurements of auditory and visual evoked responses, where the highly correlated sources are known to be difficult to estimate.
Year
DOI
Venue
2014
10.1016/j.neuroimage.2013.09.012
NeuroImage
Keywords
Field
DocType
Magnetoencephalography,Source reconstruction,Non-Gaussian,Beamformer,Null-beamformer
Gaussian filter,Joint probability distribution,Gaussian random field,Computer science,Cognitive psychology,Algorithm,Speech recognition,Gaussian,Gaussian process,Gaussian noise,Gaussian function,Kernel density estimation
Journal
Volume
ISSN
Citations 
87
1053-8119
4
PageRank 
References 
Authors
0.44
24
6
Name
Order
Citations
PageRank
Hamid Reza Mohseni1754.65
Morten L. Kringelbach218521.07
Mark W Woolrich3172394.51
Adam Baker440.44
Tipu Z. Aziz516813.34
Penny Probert Smith6153.59