Advances in EEG filtering algorithms enable analysis of EEG recorded during motor tasks. Although methods such as artifact subspace reconstruction (ASR) can remove transient artifacts automatically, there is virtually no knowledge about how the vigor of bodily movements affects ASRs performance and optimal cut-off parameter selection process. We compared the ratios of removed and reconstructed EEG recorded during a cognitive task, single-leg stance, and fast walking using ASR with 10 cut-off parameters versus visual inspection. Furthermore, we used the repeatability and dipolarity of independent components to assess their quality and an automatic classification tool to assess the number of brain-related independent components. The cut-off parameter equivalent to the ratio of EEG removed in manual cleaning was strictest for the walking task. The quality index of independent components, calculated using RELICA, reached a maximum plateau for cut-off parameters of 10 and higher across all tasks while dipolarity was largely unaffected. The number of independent components within each task remained constant, regardless of the cut-off parameter used. Surprisingly, ASR performed better in motor tasks compared with non-movement tasks. The quality index seemed to be more sensitive to changes induced by ASR compared to dipolarity. There was no benefit of using cut-off parameters less than 10.

EEG 过滤算法的进步使得能够对运动任务期间记录的 EEG 进行分析。尽管伪像子空间重建 (ASR) 等方法可以自动去除瞬态伪像，但几乎没有关于身体运动的活力如何影响 ASR 性能和最佳截止参数选择过程的知识。我们比较了使用具有 10 个截止参数的 ASR 与视觉检查在认知任务、单腿站立和快速步行期间记录的移除和重建 EEG 的比率。此外，我们使用独立组件的可重复性和偶极性来评估其质量，并使用自动分类工具来评估与大脑相关的独立组件的数量。相当于手动清洁中去除的 EEG 比率的截止参数对于步行任务是最严格的。使用 RELICA 计算的独立组件的质量指数在所有任务中达到了 10 或更高的截止参数的最大平台，而偶极性基本上不受影响。无论使用何种截止参数，每个任务中独立组件的数量保持不变。令人惊讶的是，与非运动任务相比，ASR 在运动任务中表现更好。与偶极相比，质量指数似乎对 ASR 引起的变化更敏感。使用小于 10 的截止参数没有任何好处。令人惊讶的是，与非运动任务相比，ASR 在运动任务中表现更好。与偶极相比，质量指数似乎对 ASR 引起的变化更敏感。使用小于 10 的截止参数没有任何好处。令人惊讶的是，与非运动任务相比，ASR 在运动任务中表现更好。与偶极相比，质量指数似乎对 ASR 引起的变化更敏感。使用小于 10 的截止参数没有任何好处。