Automated EEG mega-analysis I: Spectral and amplitude characteristics across studies

Significant achievements have been made in the fMRI field by pooling statistical results from multiple studies (meta-analysis). More recently, fMRI standardization efforts have focused on enabling the combination of raw fMRI data across studies (mega-analysis), with the hope of achieving more detailed insights. However, it has not been clear if such analyses in the EEG field are possible or equally fruitful. Here we present the results of a large-scale EEG mega-analysis using 18 studies from six sites representing several different experimental paradigms. Our results show that EEG mega-analysis is possible and can provide unique insights unavailable in single studies. Standardized EEG was subjected to a fully-automated pipeline that reduces line noise, interpolates noisy channels, performs robust referencing, removes eye-activity, and further identifies outlier signals. We then define channel dispersion measures to assess the comparability of data across studies and observe the effect of various processing steps on dispersion measures. Using ICA-based dipolar sources, we also observe consistent differences in overall frequency baseline amplitudes across brain areas. For example, we observe higher alpha in posterior vs anterior regions and higher beta in temporal regions. We also observe consistent differences in the slope of aperiodic portion of the EEG spectrum across brain areas. This work demonstrates that EEG mega-analysis can enable investigations of brain dynamics in a more generalized fashion, opening the door for both expanded EEG mega-analysis as well as large-scale EEG meta-analysis. In a companion paper, we apply mega-analysis to assess commonalities in event-related EEG features across studies.