Urigüen Jose Antonio, Garcia-Zapirain Begoña
Deustotech-Life (eVida Research Group), University of Deusto, Facultad de Ingeniería, 4a Planta Avda/Universidades 24, 48007 Bilbao, Spain.
J Neural Eng. 2015 Jun;12(3):031001. doi: 10.1088/1741-2560/12/3/031001. Epub 2015 Apr 2.
This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts. We first introduce background knowledge on the characteristics of EEG activity, of the artifacts and of the EEG measurement model. Then, we present algorithms commonly employed in the literature and describe their key features. Lastly, principally on the basis of the results provided by various researchers, but also supported by our own experience, we compare the state-of-the-art methods in terms of reported performance, and provide guidelines on how to choose a suitable artifact removal algorithm for a given scenario. With this review we have concluded that, without prior knowledge of the recorded EEG signal or the contaminants, the safest approach is to correct the measured EEG using independent component analysis-to be precise, an algorithm based on second-order statistics such as second-order blind identification (SOBI). Other effective alternatives include extended information maximization (InfoMax) and an adaptive mixture of independent component analyzers (AMICA), based on higher order statistics. All of these algorithms have proved particularly effective with simulations and, more importantly, with data collected in controlled recording conditions. Moreover, whenever prior knowledge is available, then a constrained form of the chosen method should be used in order to incorporate such additional information. Finally, since which algorithm is the best performing is highly dependent on the type of the EEG signal, the artifacts and the signal to contaminant ratio, we believe that the optimal method for removing artifacts from the EEG consists in combining more than one algorithm to correct the signal using multiple processing stages, even though this is an option largely unexplored by researchers in the area.
本文对用于去除脑电图（EEG）中主要干扰源的伪迹去除算法进行了广泛综述，这些干扰源具体包括眼电、肌电和心电伪迹。我们首先介绍关于EEG活动特征、伪迹以及EEG测量模型的背景知识。然后，我们呈现文献中常用的算法并描述其关键特性。最后，主要基于不同研究者给出的结果，同时也结合我们自己的经验，我们在报告的性能方面比较了当前的先进方法，并针对如何为给定场景选择合适的伪迹去除算法提供指导。通过本次综述，我们得出结论：在没有关于记录的EEG信号或污染物的先验知识的情况下，最安全的方法是使用独立成分分析来校正测量得到的EEG——确切地说，是一种基于二阶统计量的算法，如二阶盲辨识（SOBI）。其他有效的替代方法包括基于高阶统计量的扩展信息最大化（InfoMax）和独立成分分析器的自适应混合（AMICA）。所有这些算法在模拟中，更重要的是在受控记录条件下收集的数据中都已证明特别有效。此外，只要有先验知识可用，那么就应该使用所选方法的约束形式以纳入此类额外信息。最后，由于哪种算法性能最佳高度依赖于EEG信号的类型、伪迹以及信号与污染物的比率，我们认为从EEG中去除伪迹的最优方法在于组合多种算法，通过多个处理阶段来校正信号，尽管这是该领域研究人员很大程度上未探索的一种选择。
