Robust detrending, rereferencing, outlier detection, and inpainting for multichannel data.
Robust detrending, rereferencing, outlier detection, and inpainting for multichannel data.
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DOI：
10.1016/j.neuroimage.2018.01.035
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发表时间：
2018-05-15
期刊：
影响因子：
5.7
通讯作者：
Arzounian D
中科院分区：
文献类型：
--
作者：
de Cheveigné A;Arzounian D
脑电图（EEG）、脑磁图（MEG）和相关技术容易出现毛刺、缓慢漂移、台阶等，污染数据并干扰分析和解释。这些工件通常在预处理阶段处理，试图删除它们或最小化它们的影响。本文为此提供了一组有用的技术：鲁棒的去趋势，鲁棒的重引用，离群值检测，数据插值（修复），步骤删除，和滤波器振铃伪影去除。这些技术提供了一种较少浪费的替代方案来丢弃损坏的试验或通道，并且它们相对不受干扰替代方法（例如过滤）的伪影的影响。强大的去趋势功能允许去除缓慢漂移和共模信号，同时避免毛刺的有害影响。健壮的重引用减少了工件对引用的影响。修复允许基于在完整部分上估计的相关性结构从完整部分内插损坏的数据。离群值检测允许识别损坏的部分。阶跃消除修复了一些MEG系统常见的高振幅通量跳变伪影。消除振铃可抑制抗混叠滤波器对毛刺（阶跃、脉冲）的振铃响应。使用合成数据和来自真实的EEG和MEG系统的数据来说明和评估该方法的性能。这些方法主要是自动的，几乎不需要调整，可以大大提高数据的质量。预处理是EEG和MEG数据分析的基础。用于数据预处理的鲁棒方法不受毛刺和伪影的影响。方法包括强大的去趋势，重新引用，修复和步骤删除。这些方法是有效的，并与标准技术，如伊卡的补充。
Electroencephalography (EEG), magnetoencephalography (MEG) and related techniques are prone to glitches, slow drift, steps, etc., that contaminate the data and interfere with the analysis and interpretation. These artifacts are usually addressed in a preprocessing phase that attempts to remove them or minimize their impact. This paper offers a set of useful techniques for this purpose: robust detrending, robust rereferencing, outlier detection, data interpolation (inpainting), step removal, and filter ringing artifact removal. These techniques provide a less wasteful alternative to discarding corrupted trials or channels, and they are relatively immune to artifacts that disrupt alternative approaches such as filtering. Robust detrending allows slow drifts and common mode signals to be factored out while avoiding the deleterious effects of glitches. Robust rereferencing reduces the impact of artifacts on the reference. Inpainting allows corrupt data to be interpolated from intact parts based on the correlation structure estimated over the intact parts. Outlier detection allows the corrupt parts to be identified. Step removal fixes the high-amplitude flux jump artifacts that are common with some MEG systems. Ringing removal allows the ringing response of the antialiasing filter to glitches (steps, pulses) to be suppressed. The performance of the methods is illustrated and evaluated using synthetic data and data from real EEG and MEG systems. These methods, which are mainly automatic and require little tuning, can greatly improve the quality of the data. Preprocessing is essential for EEG and MEG data analysis. Robust methods for data preprocessing are not affected by glitches and artifacts. Methods include robust detrending, rereferencing, inpainting and step removal. These methods are effective and complementary with standard techniques such as ICA.
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