Artifact removal from the EEG traces is a constant focus for many EEG researchers. Trained researchers are consistent among each other with an ICC above 0.80 for the extraction of certain power values (Hatz et al., 2015). With the increasingly expanding number of datasets, much effort is being put into automated detection and removal of artifacts. There are a variety of algorithms, based on either statistical thresholding, either fixed or adaptive, or using Bayesian approaches. Individual level ICA based on Blind Source Separation (BSS) seems to have established a dominant position for removal of various types of fixed‐source artifacts (Delorme et al., 2007; Nolan et al., 2010), with several methods for automated artifact IC detection (Nolan et al., 2010; Pion‐Tonachini et al., 2019). Recent complementary methods such as Artifact Subspace Reconstruction (ASR) propose solutions to remove transient large amplitude noise 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
