ZapLine: A simple and effective method to remove power line artifacts
ZapLine: A simple and effective method to remove power line artifacts
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DOI：
10.1016/j.neuroimage.2019.116356
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发表时间：
2020-02-15
期刊：
影响因子：
5.7
通讯作者：
de Cheveigne, Alain
中科院分区：
文献类型：
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作者：
de Cheveigne, Alain
电力线伪迹是动物和人类电生理学的祸根。有许多方法可以帮助减弱或消除它们，但每种方法都有自己的一套缺点。在这篇简短的笔记中，我提出了一种简单的方法，它结合了光谱和空间滤波的优点，同时最小化了它们的缺点。使用完美重构滤波器组将数据分成两部分，一部分没有噪声，另一部分受到线状伪影的污染。受伪影污染的流由空间滤波器处理以投影出线分量，并被添加到无噪声部分以获得干净的数据。该方法适用于多通道数据，如脑电(EEG)、脑磁图(MEG)或多通道局部场电位(LFP)。我简要回顾了过去的方法，指出了它们的缺点，描述了新方法，并使用合成数据和真实数据评估了结果。
Power line artifacts are the bane of animal and human electrophysiology. A number of methods are available to help attenuate or eliminate them, but each has its own set of drawbacks. In this brief note I present a simple method that combines the advantages of spectral and spatial filtering, while minimizing their downsides. A perfect-reconstruction filterbank is used to split the data into two parts, one noise-free and the other contaminated by line artifact. The artifact-contaminated stream is processed by a spatial filter to project out line components, and added to the noise-free part to obtain clean data. This method is applicable to multichannel data such as electroencephalography (EEG), magnetoencephalography (MEG), or multichannel local field potentials (LFP). I briefly review past methods, pointing out their drawbacks, describe the new method, and evaluate the outcome using synthetic and real data.