Non-Gaussian probabilistic MEG source localisation based on kernel density estimation.

Journal article

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 ME...

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Authors: Mohseni, H; Kringelbach, M; Woolrich, M; Baker, A; Aziz, T

• Publication status: Published
• Publisher: Academic Press Inc.
• Journal: NeuroImage
• Volume: 87
• Pages: 444-464
• Publication date: 2014-02-01
• DOI: 10.1016/j.neuroimage.2013.09.012
• EISSN: 1095-9572
• ISSN: 1053-8119
• Language: English
• Keywords: Models, Neurological; Humans; Signal Processing, Computer-Assisted; Magnetoencephalography; Bayes Theorem; Brain; Algorithms; Computer Simulation