Abstract
EEG preprocessing approaches have not been standardized, and even those studies that follow best practices contain variations in the ways that the recommended methods are applied. An open question for researchers is how sensitive the results of EEG analyses are to preprocessing methods and parameters. To address this issue, we analyze the effect of preprocessing methods on downstream EEG analysis using several simple signal and event-related measures. Signal measures include recording-level channel amplitudes, study-level channel amplitude dispersion, and recording spectral characteristics. Event-related methods include ERPs and ERSPs and their correlations across methods for a diverse set of stimulus events. Our analysis also assesses differences in residual signals both in the time and spectral domains after blink artifacts have been removed. Using fully automated pipelines, we evaluate these measures across 17 EEG studies for two ICA-based preprocessing approaches (LARG, MARA) plus two variations of Artifact Subspace Reconstruction (ASR). Although the general structure of the results is similar across these preprocessing methods, there are significant differences, particularly in the low-frequency spectral features and in the residuals left by blinks. These results argue for detailed reporting of processing details and for using a federation of processing pipelines to quantify effects of processing choices.
Footnotes
↵1 This paper was submitted on Nov. 1, 2019. This work was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-10-2-0022 (CAST 076910227001). Computational support was provided by UTSA Office of Information Technology. Authors N.B., T.M., and C.K. were paid salaries or otherwise hold a financial interest in Intheon.
