EEG artifact removal—state-of-the-art and guidelines

最大熵 工件(错误) 计算机科学 脑电图 独立成分分析 鉴定(生物学) 人工智能 信号(编程语言) 组分(热力学) 最大化 盲信号分离 模式识别(心理学) 干扰(通信) 语音识别 机器学习 数学 精神科 心理学 物理 数学优化 频道(广播) 热力学 生物 植物 程序设计语言 计算机网络
作者
José Antonio Urigüen,Begonya García-Zapirain
出处
期刊:Journal of Neural Engineering [IOP Publishing]
卷期号:12 (3): 031001-031001 被引量:742
标识
DOI:10.1088/1741-2560/12/3/031001
摘要

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.
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