Objective: Artifact subspace reconstruction (ASR) is an automatic, online-capable, component-based method that can effectively remove transient or large-amplitude artifacts contaminating electroencephalographic (EEG) data. However, the effectiveness of ASR and the optimal choice of its parameter have not been systematically evaluated and reported, especially on actual EEG data. Methods: This paper systematically evaluates ASR on 20 EEG recordings taken during simulated driving experiments. Independent component analysis (ICA) and an independent component classifier are applied to separate artifacts from brain signals to quantitatively assess the effectiveness of the ASR. Results: ASR removes more eye and muscle components than brain components. Even though some eye and muscle components retain after ASR cleaning, the power of their temporal activities is reduced. Study results also showed that ASR cleaning improved the quality of a subsequent ICA decomposition. Conclusions: Empirical results show that the optimal ASR parameter is between 20 and 30, balancing between removing non-brain signals and retaining brain activities. Significance: With an appropriate choice of parameter, ASR can be a powerful and automatic artifact removal approach for offline data analysis or online real-time EEG applications such as clinical monitoring and brain–computer interfaces.

中文翻译：

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用于多通道 EEG 记录中自动伪影成分去除的伪影子空间重建评估

目标：伪影子空间重建 (ASR) 是一种自动、在线、基于组件的方法，可以有效地去除污染脑电图 (EEG) 数据的瞬态或大振幅伪影。然而，ASR 的有效性及其参数的最佳选择尚未被系统地评估和报告，尤其是在实际的 EEG 数据上。方法：本文系统地评估了模拟驾驶实验期间采集的 20 个 EEG 记录的 ASR。独立分量分析 (ICA) 和独立分量分类器用于从大脑信号中分离伪影，以定量评估 ASR 的有效性。结果：ASR 去除的眼睛和肌肉成分多于大脑成分。即使在 ASR 清洁后保留了一些眼睛和肌肉成分，他们的时间活动的力量减少了。研究结果还表明，ASR 清洗提高了后续 ICA 分解的质量。结论：实证结果表明，最佳 ASR 参数在 20 到 30 之间，在去除非大脑信号和保留大脑活动之间取得平衡。意义：具有适当的参数选择，ASR可以是用于离线数据分析或在线实时EEG应用的强大和自动的伪影方法，例如临床监测和脑 - 计算机接口。