Bailey N W, Biabani M, Hill A T, Miljevic A, Rogasch N C, McQueen B, Murphy O W, Fitzgerald P B
Central Clinical School Department of Psychiatry, Monash University, Camberwell, Victoria, Australia; School of Medicine and Psychology, The Australian National University, Canberra, ACT, Australia; Monarch Research Institute Monarch Mental Health Group, Sydney, NSW, Australia.
The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Victoria, Australia.
Clin Neurophysiol. 2023 May;149:178-201. doi: 10.1016/j.clinph.2023.01.017. Epub 2023 Feb 13.
Electroencephalographic (EEG) data are often contaminated with non-neural artifacts which can confound experimental results. Current artifact cleaning approaches often require costly manual input. Our aim was to provide a fully automated EEG cleaning pipeline that addresses all artifact types and improves measurement of EEG outcomes METHODS: We developed RELAX (the Reduction of Electroencephalographic Artifacts). RELAX cleans continuous data using Multi-channel Wiener filtering [MWF] and/or wavelet enhanced independent component analysis [wICA] applied to artifacts identified by ICLabel [wICA_ICLabel]). Several versions of RELAX were compared using three datasets (N = 213, 60 and 23 respectively) against six commonly used pipelines across a range of artifact cleaning metrics, including measures of remaining blink and muscle activity, and the variance explained by experimental manipulations after cleaning.
RELAX with MWF and wICA_ICLabel showed amongst the best performance at cleaning blink and muscle artifacts while preserving neural signal. RELAX with wICA_ICLabel only may perform better at differentiating alpha oscillations between working memory conditions.
RELAX provides automated, objective and high-performing EEG cleaning, is easy to use, and freely available on GitHub.
We recommend RELAX for data cleaning across EEG studies to reduce artifact confounds, improve outcome measurement and improve inter-study consistency.
脑电图（EEG）数据常被非神经伪迹污染，这可能会混淆实验结果。当前的伪迹清理方法通常需要昂贵的人工输入。我们的目标是提供一种全自动的EEG清理流程，该流程能处理所有类型的伪迹并改善EEG结果的测量。方法：我们开发了RELAX（脑电图伪迹减少工具）。RELAX使用多通道维纳滤波[MWF]和/或应用于由ICLabel识别的伪迹的小波增强独立成分分析[wICA_ICLabel]来清理连续数据。使用三个数据集（分别为N = 213、60和23），针对六种常用流程，在一系列伪迹清理指标上比较了RELAX的几个版本，这些指标包括剩余眨眼和肌肉活动的测量，以及清理后实验操作所解释的方差。
结合MWF和wICA_ICLabel的RELAX在清理眨眼和肌肉伪迹同时保留神经信号方面表现最佳。仅结合wICA_ICLabel的RELAX在区分工作记忆条件下的阿尔法振荡方面可能表现更好。
RELAX提供自动化、客观且高性能的EEG清理，易于使用，可在GitHub上免费获取。
我们建议在整个EEG研究中使用RELAX进行数据清理，以减少伪迹干扰，改善结果测量并提高研究间的一致性。
