The extracranial EEG/MEG methods have to contend with the inherent uncertainty of the source estimation, their excellent temporal resolution notwithstanding (Cohen and Halgren, 2009). The estimation uncertainty results from a combination of influences including the signal propagation from the generator to the sensors, overlapping activity from different sources that is recorded by each sensor, and incomplete field sampling. Compared to EEG, MEG is little affected by the tissue interposed between the generators and sensors (Hämäläinen et al., 1993; Liu et al., 2002), and application of physiologically reasonable solution constraints permits disambiguation of the inverse problem in the context of multimodal integration (Dale and Sereno, 1993; Dale et al., 2000). The uncertainty is further influenced by the employed model which determines the permitted generator locations and the degree of their distribution, along with other parameters. The aMEG approach relies on the assumption that the synaptic currents giving rise to the summated MEG signal observed on the scalp are confined to the cortical ribbon (Dale et al., 2000). It applies a real head model obtained by reconstructing each person’s cortical surface from
