Figure 1.

Illustrative comparison of region of interest (ROI) and EMS filtering analyses applied to a representative subject. The mean time course of an ROI (A), fixed spatial filter (B), and evolving spatial filter (C) for the difference between the lag-9 and control conditions in the data from Marti et al (2012). Just above each of the panels is the topography of the average spatial filter within each of three different temporal windows: 0.25 to 0.30 sec, 1.20 to 1.25 sec, and 1.40 to 1.45 sec. Only the magnetometers are shown for clarity. Note that the ROI “spatial filter” is discrete and binary, and is identical at all time points in the epoch. The time course in (B) was derived using a stationary filter computed over the data in a specific time window (EMSf-st), with the spatial filter defined as the mean difference between the lag-9 and control conditions between 1.35 and 1.45 sec. The grayscale error boundary extends to 99% confidence (on a t distribution, df = 9). Note that the difference is maximized in the time window over which the spatial filter was defined. Note also that, as in (A), the spatial filter is identical at all time points, but that unlike (A), the spatial filter is continuous-valued rather than discrete. The time course in (C) was derived from the output of canonical EMS filtering. The dashed line shows the time course of the stationary template used in panel B (for visual comparison with panel B). Note in panel C that the spatial filters are continuous-valued and are also changing across time in the epoch (the spatial filter is computed independently at each time point in the epoch according to the objective function, which in this case is simply the difference between the lag-9 and control conditions).

Schurger et al. BMC Neuroscience 2013 14:122   doi:10.1186/1471-2202-14-122
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