How do the resting EEG preprocessing states affect the outcomes of postprocessing?

TL;DR

This study investigates how different EEG preprocessing states, specifically insufficient or excessive artifact removal, influence the accuracy of postprocessing outcomes in spectral, spatial, and connectivity analyses, highlighting the importance of optimal preprocessing.

Contribution

It introduces a simulation framework to quantify the effects of preprocessing quality on EEG postprocessing and explores PaLOSi as a potential quality metric.

Findings

Preprocessing states significantly affect spectral and connectivity measures.

PaLOSi correlates with the deviations caused by preprocessing errors.

Optimal preprocessing is crucial for reliable EEG postanalysis.

Abstract

Plenty of artifact removal tools and pipelines have been developed to correct the EEG recordings and discover the values below the waveforms. Without visual inspection from the experts, it is susceptible to derive improper preprocessing states, like the insufficient preprocessed EEG (IPE), and the excessive preprocessed EEG (EPE). However, little is known about the impacts of IPE or EPE on the postprocessing in the frequency, spatial and temporal domains, particularly as to the spectra and the functional connectivity (FC) analysis. Here, the clean EEG (CE) was synthesized as the ground truth based on the New-York head model and the multivariate autoregressive model. Later, the IPE and the EPE were simulated by injecting the Gaussian noise and losing the brain activities, respectively. Then, the impacts on postprocessing were quantified by the deviation caused by the IPE or EPE from the CE as to the 4 temporal statistics, the multichannel power, the cross spectra, the dispersion of source imaging, and the properties of scalp EEG network. Lastly, the association analysis was performed between the PaLOSi metric and the varying trends of postprocessing with the evolution of preprocessing states. This study shed light on how the postprocessing outcomes are affected by the preprocessing states and PaLOSi may be a potential effective quality metric.