from the data (Chang et al., 2019). Although many automated artifact removal techniques still require visual confirmation, fully automated algorithms may actually be in good agreement with visual inspection for high density recordings (Hatz et al., 2015). This opens up possibilities for large‐scale endeavors such as ENIGMA‐EEG to implement fully automated pipelines such as the one implemented by one of us (SJB) (https://github.com/sjburwell/eeg_commander) and others (https://www.frontiersin.org/articles/10.3389/fnins.2018.00097/full; https://www.frontiersin.org/articles/10.3389/fninf.2015.00016/full). We note, however, that there is no agreed upon gold standard for automated artifact removal yet.
