Young Investigator Group Intuitive XR, Neuroadaptive Human-Computer Interaction, Institute of Medical Technology, BTU Cottbus-Senftenberg, Cottbus, Germany.
Biopsychology and Neuroergonomics, Institute of Psychology and Ergonomics, TU Berlin, Berlin, Germany.
Sci Rep. 2024 Jun 19;14(1):14119. doi: 10.1038/s41598-024-64919-3.
Electroencephalography (EEG) studies increasingly utilize more mobile experimental protocols, leading to more and stronger artifacts in the recorded data. Independent Component Analysis (ICA) is commonly used to remove these artifacts. It is standard practice to remove artifactual samples before ICA to improve the decomposition, for example using automatic tools such as the sample rejection option of the AMICA algorithm. However, the effects of movement intensity and the strength of automatic sample rejection on ICA decomposition have not been systematically evaluated. We conducted AMICA decompositions on eight open-access datasets with varying degrees of motion intensity using varying sample rejection criteria. We evaluated decomposition quality using mutual information of the components, the proportion of brain, muscle, and 'other' components, residual variance, and an exemplary signal-to-noise ratio. Within individual studies, increased movement significantly decreased decomposition quality, though this effect was not found across different studies. Cleaning strength significantly improved the decomposition, but the effect was smaller than expected. Our results suggest that the AMICA algorithm is robust even with limited data cleaning. Moderate cleaning, such as 5 to 10 iterations of the AMICA sample rejection, is likely to improve the decomposition of most datasets, regardless of motion intensity.
脑电图（EEG）研究越来越多地采用更具移动性的实验方案，导致记录数据中的伪影越来越多，越来越强。独立成分分析（ICA）通常用于去除这些伪影。在进行 ICA 分解之前，去除伪影样本是标准做法，例如使用 AMICA 算法的样本拒绝选项等自动工具。然而，运动强度和自动样本拒绝的强度对 ICA 分解的影响尚未得到系统评估。我们使用不同的样本拒绝标准，对八个具有不同运动强度的公开数据集进行了 AMICA 分解。我们使用组件的互信息、大脑、肌肉和“其他”组件的比例、残余方差以及示例信噪比来评估分解质量。在个体研究中，运动的增加显著降低了分解质量，但在不同研究中并未发现这种影响。清理强度显著改善了分解，但效果低于预期。我们的结果表明，即使数据清理有限，AMICA 算法也很稳健。适度的清理，例如 AMICA 样本拒绝的 5 到 10 次迭代，可能会改善大多数数据集的分解，而与运动强度无关。
