Preprocessing of raw EEG signals included a low pass (1 Hz) and high pass (100 Hz) filtering with a zero-phase notch filter to remove the 60 Hz line noise. EEG signals were re-referenced to the average across EEG montage to permit use of MNE-ICAlabel package that identifies and removes non-brain artifacts. Independent components analysis was performed using infomax method generating 15 independent components for use in MNE-ICAlabel artifact processing. Only independent components classified as ‘brain’ or ‘other’ were retained for signal reconstruction, while those labeled as ‘artifacts’ were removed. Python package MNE was used for preprocessing steps. Coding infrastructure for this project which includes EEG preprocessing, PAC comodulogram image generation, and statistical analyses was built using Python and is available at https://github.com/lifepupil.
