After channel pruning, noisy segments of data, i.e., segments that contained gross artifacts such as muscle twitching or swallowing were removed from the data. At this stage, the data were (i) decomposed by extended infomax ICA using the function runica, as implemented in EEGLAB, and then segmented into epochs associated with presentation of the high spatial frequency Gabor patch (8 cycles/degree); epochs were 800 ms long (−100 ms pre-stimulus), and baseline corrected by subtracting the mean of the 100 ms pre-stimulus interval, and (ii) segmented into epochs as described above and then transformed into CSD estimates (measured in μV/cm2) using a spherical spline surface Laplacian (Perrin et al., 1989) as implemented by Kayser and Tenke (2006), Kayser (2009), with a spline-smoothing coefficient (λ) of 1.0−5. Both sets of data were then low-pass filtered (<30 Hz).
