Fig. 4.

A comparison of analytical thresholds for a mass-univariate test based on three different estimates of the number of independent sources for a family wise error rate of 5%. The three estimates are the total number of sources (A), the number of sensors (B) and the number of unique extremal pairs (C). Different symbols represent different grid spacing (circles, 20 mm, triangles 10 mm, and squares 5 mm) and different colours represent different regions of interest (red whole brain, green occipital lobe, and blue Heschl's gyrus). The dotted lines show the ideal (exact) match between permutation and analytical thresholds, and points below this line indicate that analytical thresholds give conservative (larger) thresholds than required by permutation testing. The false positive rate (assuming that all sources are independent) is, as one would expect, always conservative but becomes more accurate as the number of sources decreases. The assumption that there are as many independent sources as there are sensors (B) gives inexact thresholds, which are generally conservative for smaller regions of interest (blue) and too liberal for larger ROIs (red). In panel C, the number of independent sources is based on the unique extremal heuristic, which provides efficient thresholds across all grid spacing and regions of interest. Panel D shows the actual and desired (dotted) error rates for each sort of threshold reported in panels A, B and C.