Mapping human brain function: a comparison between Variational Bayes Techniques and LCMV Beamformer

In the last years several hierarchical Bayesian approaches to the MEG/EEG inverse problem have provided for a relevant contribution to the field of MEG/EEG source localization (Friston et al., 2008b; Wipf et al., 2010). While several methods show applicability under specific conditions, none is optimal without prior information. Meaningful results are bound to previously acquired information. In this work we used simulated MEG data to compare three Variational Bayes reconstruction algorithms implemented within the SPM software preprocessing framework (available from http://www.fil.ion.ucl.ac.uk/spm/): two approaches involving the search for optimal mixtures of anatomically defined priors (Greedy Search (GS) and Automatic Relevance Determination (ARD)) (Friston et al., 2008a) and a third approach using a single empirical prior based on the well established LCMV Beamformer technique (Van Veen et al., 1997), that we denominated Empirical Bayes Beamformer (EBB). Our parameters of interest were: 1. Number of simulated dipoles (1 to 3), 2. Relative position between dipoles (bilaterally symmetric versus random locations) 3. Dipole time-course correlation level (high/low). Each parameter configuration set was tested with 5 levels of SNR (from -30 to +10 dB) and 50 dipole position sets