Biopsychology and Neuroergonomics, Institute of Psychology and Ergonomics, TU Berlin, Berlin, Germany.
Center for Advanced Neurological Engineering, University of California San Diego, La Jolla, CA, USA.
Eur J Neurosci. 2021 Dec;54(12):8406-8420. doi: 10.1111/ejn.14992. Epub 2020 Oct 15.
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.
最近 EEG 硬件和分析方法的发展允许在固定和移动环境中进行记录。无论实验设置如何，EEG 记录都受到噪声的污染，在数据可以进行功能解释之前，必须去除这些噪声。独立成分分析（ICA）是一种常用的工具，可用于从数据中去除眼动、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提高分解效果的一个关键预处理步骤，这一点已经在之前的研究中进行了探讨。然而，迄今为止，没有研究比较过移动和固定实验在 ICA 分解预处理方面的不同要求。因此，我们评估了 EEG 实验中的运动、通道数量以及预处理过程中的高通滤波器截止频率如何影响 ICA 分解。我们发现，对于常用设置（固定实验、64 个通道、0.5 Hz 滤波器），ICA 结果是可以接受的。然而，在移动实验中应使用高达 2 Hz 截止频率的高通滤波器，并且更多的通道需要更高的滤波器才能达到最佳分解。在移动实验中发现的大脑 IC 较少，但即使使用低密度通道设置，使用 ICA 清洁数据也被证明是重要且有效的。基于这些结果，我们为不同的实验设置提供了指南，以改善 ICA 分解。
