Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland.
Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland.
Neuroimage. 2019 Oct 15;200:460-473. doi: 10.1016/j.neuroimage.2019.06.046. Epub 2019 Jun 21.
Electroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG recordings but mainly on account of methodological issues. In many cases, particularly in clinical, pediatric and aging populations, the EEG has a high degree of artifact contamination and the quality of EEG recordings often substantially differs between subjects. Although there exists a variety of standardized preprocessing methods to clean EEG from artifacts, currently there is no method to objectively quantify the quality of preprocessed EEG. This makes the commonly accepted procedure of excluding subjects from analyses due to exceeding contamination of artifacts highly subjective. As a consequence, P-hacking is fostered, the replicability of results is decreased, and it is difficult to pool data from different study sites. In addition, in large-scale studies, data are collected over years or even decades, requiring software that controls and manages the preprocessing of ongoing and dynamically growing studies. To address these challenges, we developed Automagic, an open-source MATLAB toolbox that acts as a wrapper to run currently available preprocessing methods and offers objective standardized quality assessment for growing studies. The software is compatible with the Brain Imaging Data Structure (BIDS) standard and hence facilitates data sharing. In the present paper we outline the functionality of Automagic and examine the effect of applying combinations of methods on a sample of resting and task-based EEG data. This examination suggests that applying a pipeline of algorithms to detect artifactual channels in combination with Multiple Artifact Rejection Algorithm (MARA), an independent component analysis (ICA)-based artifact correction method, is sufficient to reduce a large extent of artifacts.
脑电图 (EEG) 记录在大型研究中很少被包括在内。这并不是因为 EEG 记录中缺乏信息，而是主要由于方法学问题。在许多情况下，特别是在临床、儿科和老年人群中，脑电图具有高度的伪迹污染，并且 EEG 记录的质量在受试者之间经常有很大的差异。尽管存在各种标准化预处理方法来清除 EEG 中的伪迹，但目前还没有方法来客观地量化预处理后的 EEG 质量。这使得由于伪迹污染过高而将受试者排除在分析之外的常用程序变得非常主观。因此，助长了 P-值操纵，结果的可重复性降低，并且难以从不同的研究地点汇集数据。此外，在大型研究中，数据是在数年甚至数十年内收集的，这需要软件来控制和管理正在进行的和动态增长的研究的预处理。为了解决这些挑战，我们开发了 Automagic，这是一个开源的 MATLAB 工具箱，作为一个包装器来运行当前可用的预处理方法，并为不断发展的研究提供客观的标准化质量评估。该软件与脑成像数据结构 (BIDS) 标准兼容，因此便于数据共享。在本文中，我们概述了 Automagic 的功能，并检查了在静息和任务 EEG 数据样本上应用方法组合的效果。该检查表明，应用算法管道来检测伪迹通道，并结合基于独立成分分析 (ICA) 的伪迹校正方法多伪迹拒绝算法 (MARA)，足以大大减少伪迹。
