Frølich Laura, Dowding Irene
Technical University of Denmark, Lyngby, Denmark.
Technische Universität Berlin, Berlin, Germany.
Brain Inform. 2018 Mar;5(1):13-22. doi: 10.1007/s40708-017-0074-6. Epub 2018 Jan 10.
The most common approach to reduce muscle artifacts in electroencephalographic signals is to linearly decompose the signals in order to separate artifactual from neural sources, using one of several variants of independent component analysis (ICA). Here we compare three of the most commonly used ICA methods (extended Infomax, FastICA and TDSEP) with two other linear decomposition methods (Fourier-ICA and spatio-spectral decomposition) suitable for the extraction of oscillatory activity. We evaluate the methods' ability to remove event-locked muscle artifacts while maintaining event-related desynchronization in data from 18 subjects who performed self-paced foot movements. We find that all five analyzed methods drastically reduce the muscle artifacts. For the three ICA methods, adequately high-pass filtering is very important. Compared to the effect of high-pass filtering, differences between the five analyzed methods were small, with extended Infomax performing best.
减少脑电图信号中肌肉伪迹的最常见方法是线性分解信号，以便使用独立成分分析（ICA）的几种变体之一将伪迹源与神经源分离。在这里，我们将三种最常用的ICA方法（扩展信息最大化、快速独立成分分析和TDSEP）与另外两种适用于提取振荡活动的线性分解方法（傅里叶独立成分分析和时空谱分解）进行比较。我们评估了这些方法在保持18名进行自定步速足部运动的受试者数据中事件相关去同步化的同时，去除事件锁定肌肉伪迹的能力。我们发现，所有五种分析方法都能大幅减少肌肉伪迹。对于三种ICA方法，充分的高通滤波非常重要。与高通滤波的效果相比，五种分析方法之间的差异很小，扩展信息最大化表现最佳。
