To capture and quantify the GBR, a time-frequency principal components analysis (TF-PCA) approach similar to that implemented by others (Bernat et al., 2005) was adopted. In particular, total power, evoked power, and PLF measures were down-sampled to reduce the number of points (originally 1ms X 1Hz) in the TF matricies by sampling every 2 Hz (e.g., 20, 22, 24 Hz …) between 20 and 60 Hz and every 5 ms (e.g., −50, −45, −40 ms…) between −50 and 150ms. The TF-PCA was then calculated separately for total power, evoked power, and PLF by rearranging the 2-D TF data into 1-D by transposing the frequency data at the first time point (−50 ms) and concatenating it with similarly transposed vectors at all other time points. This results in 861 sample row vectors for each electrode (n=9) and each subject (n=104), which were submitted to a covariance matrix PCA implemented in Matlab (Kayser and Tenke, 2003). All components were retained and subjected to a varimax rotation, yielding orthogonal factors corresponding to major TF components. To produce interpretable TF factor loadings, 1-D factor
