Non-Gaussian probabilistic MEG source localisation based on kernel density estimation.
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.
Institute of Biomedical Engineering, School of Engineering Science, University of Oxford, Oxford, UK; Department of Psychiatry, University of Oxford, Warneford Hospital, UK.
Hamid R
HR
Mohseni
Department of Psychiatry, University of Oxford, Warneford Hospital, UK; Oxford Centre for Human Brain Activity (OHBA), Department of Psychiatry, University of Oxford, UK; Oxford Functional Neurosurgery, Nuffield Department of Surgery, John Radcliffe Hospital, Oxford, UK; CFIN/MindLab, Aarhus University, Aarhus, Denmark.
Morten L
ML
Kringelbach
Oxford Centre for Human Brain Activity (OHBA), Department of Psychiatry, University of Oxford, UK.
Mark W
MW
Woolrich
Institute of Biomedical Engineering, School of Engineering Science, University of Oxford, Oxford, UK; Oxford Centre for Human Brain Activity (OHBA), Department of Psychiatry, University of Oxford, UK.
Adam
A
Baker
Oxford Functional Neurosurgery, Nuffield Department of Surgery, John Radcliffe Hospital, Oxford, UK.
Tipu Z
TZ
Aziz
Institute of Biomedical Engineering, School of Engineering Science, University of Oxford, Oxford, UK.
Penny
P
Probert-Smith
D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D008959:Models, Neurological; D012815:Signal Processing, Computer-Assisted
Beamformer; Magnetoencephalography; Non-Gaussian; Null-beamformer; Source reconstruction