Kabbara Aya, Forde Nina, Maumet Camille, Hassan Mahmoud
LASeR - Lebanese Association for Scientific Research, Tripoli, Lebanon.
MINDig, F-35000, Rennes, France.
Neuroimage Rep. 2023 May 27;3(2):100169. doi: 10.1016/j.ynirp.2023.100169. eCollection 2023 Jun.
As an active field of research and with the development of state-of-the-art algorithms to analyze EEG datasets, the parametrization of Electroencephalography (EEG) analysis workflows has become increasingly flexible and complex, with a great variety of methodological options and tools to be selected at each step. This high analytical flexibility can be problematic as it can yield to variability in research outcomes. Therefore, growing attention has been recently paid to understand the potential impact of different methodological decisions on the reproducibility of results. In this paper, we aim to examine how sensitive the results of EEG analyses are to variations in preprocessing with different software tools. We reanalyzed the shared EEG data (N = 500) from (Williams et al., 2021) using three of the most commonly used open-source Matlab-based EEG software tools: EEGLAB, Brainstorm and FieldTrip. After reproducing the same original preprocessing workflow in each software, the resulting event-related potentials (ERPs) were qualitatively and quantitatively compared in order to examine the degree of consistency/discrepancy between software packages. Our findings show a good degree of convergence in terms of the general profile of ERP waveforms, peak latencies and effect size estimates related to specific signal features. However, considerable variability was also observed in the magnitude of the absolute voltage observed with each software package as reflected by the similarity values and observed statistical differences at particular channels and time instants. In conclusion, we believe that this study provides valuable clues to better understand the impact of the software tool on the analysis of EEG results.
作为一个活跃的研究领域，随着用于分析脑电图（EEG）数据集的先进算法的发展，脑电图分析工作流程的参数化变得越来越灵活和复杂，在每个步骤都有各种各样的方法选项和工具可供选择。这种高度的分析灵活性可能会带来问题，因为它可能导致研究结果的变异性。因此，最近人们越来越关注理解不同方法决策对结果可重复性的潜在影响。在本文中，我们旨在研究脑电图分析结果对使用不同软件工具进行预处理变化的敏感程度。我们使用三种最常用的基于Matlab的开源脑电图软件工具：EEGLAB、Brainstorm和FieldTrip，重新分析了（Williams等人，2021年）共享的脑电图数据（N = 500）。在每个软件中重现相同的原始预处理工作流程后，对所得的事件相关电位（ERP）进行了定性和定量比较，以检查软件包之间的一致程度/差异程度。我们的研究结果表明，在ERP波形的总体特征、峰值潜伏期以及与特定信号特征相关的效应大小估计方面，具有良好的收敛程度。然而，每个软件包观察到的绝对电压幅度也存在相当大的变异性，这体现在相似性值以及特定通道和时间点观察到的统计差异上。总之，我们认为这项研究为更好地理解软件工具对脑电图结果分析的影响提供了有价值的线索。
