Notes recorded when the data were originally collected guided us in visually identifying problematic data that might need to be excluded. In addition, segments containing transient artifacts and excessively small or large voltage deflections were tagged for exclusion by computer algorithm. We used a robust version of Mahalanobis distance from the robustbase package (Rousseeuw et al., 2011) in the R statistical programming environment (R Development Core Team, 2010) to identify multivariate outliers with respect to all three leads and EEG power in all bands as well as total power. Data for which the robust Mahalanobis distance fell in the upper 10th percentile of the cumulative chi-square distribution were flagged for review and excluded if visual inspection of the raw data indicated that the data were contaminated by noise (e.g., high-frequency noise) or artifacts (e.g., electrocardiogram) or if the EEG showed clear signs of drowsiness or sleep (e.g., approximately 6-Hz theta bursts, sharp vertex waves). Individual recording sites were excluded from analyses if fewer than 45 artifact-free sweeps were available.
