Chaumon Maximilien, Bishop Dorothy V M, Busch Niko A
Berlin School of Mind and Brain, Luisenstraße 56, 10117 Berlin, Germany; Institute of Medical Psychology, Charité University Medicine, Luisenstraße 57, 10117 Berlin, Germany.
Department of Experimental Psychology, University of Oxford, South Parks Road, Oxford OX1 3UD, UK.
J Neurosci Methods. 2015 Jul 30;250:47-63. doi: 10.1016/j.jneumeth.2015.02.025. Epub 2015 Mar 16.
Electroencephalographic data are easily contaminated by signals of non-neural origin. Independent component analysis (ICA) can help correct EEG data for such artifacts. Artifact independent components (ICs) can be identified by experts via visual inspection. But artifact features are sometimes ambiguous or difficult to notice, and even experts may disagree about how to categorise a particular component. It is therefore important to inform users on artifact properties, and give them the opportunity to intervene.
Here we first describe artifacts captured by ICA. We review current methods to automatically select artifactual components for rejection, and introduce the SASICA software, implementing several novel selection algorithms as well as two previously described automated methods (ADJUST, Mognon et al. Psychophysiology 2011;48(2):229; and FASTER, Nolan et al. J Neurosci Methods 2010;48(1):152).
We evaluate these algorithms by comparing selections suggested by SASICA and other methods to manual rejections by experts. The results show that these methods can inform observers to improve rejections. However, no automated method can accurately isolate artifacts without supervision. The comprehensive and interactive plots produced by SASICA therefore constitute a helpful guide for human users for making final decisions.
Rejecting ICs before EEG data analysis unavoidably requires some level of supervision. SASICA offers observers detailed information to guide selection of artifact ICs. Because it uses quantitative parameters and thresholds, it improves objectivity and reproducibility in reporting pre-processing procedures. SASICA is also a didactic tool that allows users to quickly understand what signal features captured by ICs make them likely to reflect artifacts.
脑电图数据很容易受到非神经源性信号的污染。独立成分分析（ICA）有助于校正脑电图数据中的此类伪迹。人工伪迹独立成分（IC）可由专家通过目视检查来识别。但人工伪迹特征有时模棱两可或难以察觉，甚至专家对于如何对特定成分进行分类也可能存在分歧。因此，告知用户人工伪迹属性并让他们有机会进行干预很重要。
在此，我们首先描述通过ICA捕获的人工伪迹。我们回顾了当前自动选择要剔除的人工成分的方法，并介绍了SASICA软件，该软件实现了几种新颖的选择算法以及两种先前描述的自动化方法（ADJUST，Mognon等人，《心理生理学》，2011年；48(2):229；以及FASTER，Nolan等人，《神经科学方法杂志》，2010年；48(1):152）。
我们通过将SASICA和其他方法建议的选择与专家的人工剔除结果进行比较，来评估这些算法。结果表明，这些方法可以为观察者提供信息以改进剔除效果。然而，没有一种自动化方法能够在无监督的情况下准确地分离人工伪迹。因此，SASICA生成的全面且交互式的图表为人工用户做出最终决策提供了有益的指导。
在脑电图数据分析之前剔除IC不可避免地需要一定程度的监督。SASICA为观察者提供详细信息以指导人工伪迹IC的选择。由于它使用定量参数和阈值，因此提高了报告预处理程序时
