Automagic: Standardized preprocessing of big EEG data
Automagic: Standardized preprocessing of big EEG data
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DOI：
10.1016/j.neuroimage.2019.06.046
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发表时间：
2019-10-15
期刊：
影响因子：
5.7
通讯作者：
Langer, Nicolas
中科院分区：
文献类型：
--
作者：
Pedroni, Andreas;Bahreini, Amirreza;Langer, Nicolas
脑电图（EEG）记录很少被纳入大规模研究。这可能不是由于脑电图记录中缺乏信息，而主要是由于方法问题。在许多情况下，特别是在临床，儿科和老年人群中，脑电图具有高度的人工污染，并且脑电图记录的质量通常在受试者之间存在很大差异。虽然有多种标准化的预处理方法来清除脑电信号中的伪影，但目前还没有一种方法可以客观地量化预处理后的脑电信号的质量。这使得由于人工制品污染超标而将受试者排除在分析之外的普遍接受的程序非常主观。因此，P-hacking被助长，结果的可重复性降低，并且难以汇集来自不同研究地点的数据。此外，在大规模研究中，数据是在数年甚至数十年内收集的，这就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。为了应对这些挑战，我们开发了Automagic，这是一个开源的MATLAB工具箱，作为包装器来运行当前可用的预处理方法，并为不断增长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，并检查了在静息和基于任务的EEG数据样本上应用组合方法的效果。这项研究表明，结合基于独立分量分析（ICA）的伪影校正方法——多重伪影抑制算法（MARA），应用一系列算法来检测伪影通道，足以在很大程度上减少伪影。
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 functionally 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.