Identifying key factors for improving ICA-based decomposition of EEG data in mobile and stationary experiments
Identifying key factors for improving ICA-based decomposition of EEG data in mobile and stationary experiments
复制标题
DOI：
10.1111/ejn.14992
复制
发表时间：
2020-10-15
影响因子：
3.4
通讯作者：
Gramann, Klaus
中科院分区：
文献类型：
--
作者：
Klug, Marius;Gramann, Klaus
EEG硬件和分析方法的最新发展允许在固定和移动的设置中进行记录。无论实验设置如何，EEG记录都被噪声污染，在数据可以被功能性解释之前必须去除噪声。独立分量分析（伊卡）是一种常用的工具，用于从数据中去除诸如眼球运动、肌肉活动和外部噪声等伪影，并在EEG有效脑源水平上分析活动。过滤数据的有效性是一个关键的预处理步骤，以改善以前已经研究过的分解。然而，没有研究到目前为止比较了移动的和固定的实验伊卡分解的预处理的不同要求。因此，我们评估了如何在EEG实验中的运动，通道的数量，以及高通滤波器截止预处理过程中的影响伊卡分解。我们发现，对于常用的设置（静态实验，64通道，0.5 Hz滤波器），伊卡的结果是可以接受的。然而，在移动的实验中应使用截止频率高达2 Hz的高通滤波器，并且更多的通道需要更高的滤波器以达到最佳分解。在移动的实验中发现的大脑IC较少，但使用伊卡清理数据已被证明是重要的，即使在低密度通道设置下也是有效的。基于结果，我们提供了不同的实验设置，提高伊卡分解的指导方针。
Recent developments in EEG hardware and analyses approaches allow for recordings in both stationary and mobile settings. Irrespective of the experimental setting, EEG recordings are contaminated with noise that has to be removed before the data can be functionally interpreted. Independent component analysis (ICA) is a commonly used tool to remove artifacts such as eye movement, muscle activity, and external noise from the data and to analyze activity on the level of EEG effective brain sources. The effectiveness of filtering the data is one key preprocessing step to improve the decomposition that has been investigated previously. However, no study thus far compared the different requirements of mobile and stationary experiments regarding the preprocessing for ICA decomposition. We thus evaluated how movement in EEG experiments, the number of channels, and the high-pass filter cutoff during preprocessing influence the ICA decomposition. We found that for commonly used settings (stationary experiment, 64 channels, 0.5 Hz filter), the ICA results are acceptable. However, high-pass filters of up to 2 Hz cut-off frequency should be used in mobile experiments, and more channels require a higher filter to reach an optimal decomposition. Fewer brain ICs were found in mobile experiments, but cleaning the data with ICA has been proved to be important and functional even with low-density channel setups. Based on the results, we provide guidelines for different experimental settings that improve the ICA decomposition.