Figure 6.

EMS filtering with linear regression using a temporally defined predictor based on reaction-time data, applied to a single subject. For this analysis, the objective function used by EMS filtering performed a linear regression on the data from each sensor (i.e. a matrix of trials x samples), and returned the beta weight. The predictor variable (shown in panel A) was constructed by coding each sample with a -1 if it was in the range 200 ms before to 50 ms after RT1, a +1 if it was in the range 200 ms before to 50 ms after RT2, and a zero otherwise. Since task 1 responses and task 2 responses were made with opposite hands (left and right, respectively, for this particular subject) then this regressor should reveal response-related activity that is different for right-handed and left-handed responses. The topography of the resulting spatial filter (magnetometers) is shown in panel B, and is clearly lateralized, consistent with the coding of the regressor. Panels C and D show the surrogate time courses sorted by RT1 and RT2, respectively, with the reaction time marked by black dots. The color map goes from blue (negative) to green (zero) to red (positive). Response-related activity is plainly visible in the form of a bluish vertical band at ~ 100 to 600 ms and a reddish vertical band at ~ 1400 to 2000 ms, and shows a clear relationship with the reaction time by which the data were sorted. Panels E and F show the mean over the surrogate time courses when the trials were aligned to RT1 and RT2, respectively. Data were arbitrarily aligned to the median reaction time in each case, which is marked by a thin vertical line. A confidence boundary equal to one standard error of the mean is shown in a lighter shade of blue.

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