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.

翻译：大量伪迹去除工具和流程已被开发用于校正脑电记录并挖掘波形背后的价值。在没有专家目视检查的情况下，容易产生不当的预处理状态，例如预处理不足的脑电（IPE）和过度预处理的脑电（EPE）。然而，关于IPE或EPE对频域、空间域和时域后处理的影响，特别是对频谱和功能连接（FC）分析的影响，目前知之甚少。本研究以纽约头部模型和多元自回归模型为基础，合成了干净脑电（CE）作为真实基准。随后，通过注入高斯噪声和丢失大脑活动分别模拟了IPE和EPE。然后，通过IPE或EPE相对于CE在4个时间统计量、多通道功率、交叉频谱、源成像离散度以及头皮脑电网络特性方面引起的偏差，量化了对后处理的影响。最后，进行了PaLOSi指标与后处理随预处理状态演变趋势的关联分析。本研究揭示了后处理结果如何受预处理状态影响，且PaLOSi可能是一种潜在有效的质量度量指标。