Data for 12 subjects could not be used because equipment failure resulted in the loss of event triggers. The 422 remaining EEGs were processed offline in MATLAB (version 7.8, Mathworks Inc.) using the EEGLAB toolbox (Delorme and Makeig, 2004) and an automatic routine developed by the first two authors that integrates artifact-pruning elements from published data pipelines (Junghofer et al., 2000; Mognon et al., 2011; Nolan et al., 2010). We adopted the same general approach to Nolan et al. (2010) in that we identified artifact at a progressively finer scale, successively pruning artifact-contaminated data by examining, in order, 1) electrode, 2) time segment, 3) temporal and spatially stereotyped ocular activity (cf. Mognon et al., 2011), and finally 4) electrode/time segments. As in existing EEG processing pipelines, artifact was identified as data segments that exceeded some threshold value (e.g., 3 standard deviations) of the empirical distribution of descriptive properties (e.g., temporal deviation, maximum difference between two time-points) derived for contiguous 1-second segments of multiple-electrode EEG. The threshold was based on a robust measure of spread, the normalized median absolute deviations (Rousseeuw and Croux, 1993) to minimize the influence of outliers on the thresholds used to identify them.
