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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.