Introducing RELAX: An automated pre-processing pipeline for cleaning EEG data - Part 1: Algorithm and application to oscillations. (May 2023)

• Record Type: Journal Article
• Authors: Bailey, N.W.; Biabani, M.; Hill, A.T.; Miljevic, A.; Rogasch, N.C.; McQueen, B.; Murphy, O.W.; Fitzgerald, P.B.
• Is Part Of: Clinical neurophysiology. Volume 149(2023)
• Page Start: 178
• Page End: 201
• Publication Date: 2023-05
• Subjects: Electroencephalography -- Neural oscillations -- Pre-processing -- Artifact reduction -- Blinks -- Muscle; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2023.01.017
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: RELAX is a fully automated EEG cleaning pipeline, freely available on GitHub, and easy to use through a graphical user interface. RELAX provided amongst the best performance out of a range of common EEG pre-processing pipelines at cleaning all artifact types. RELAX provided high values for the variance explained by the difference between outcomes in common experimental manipulations. Abstract: Objective: 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. Results: 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. Conclusions: RELAX provides automated, objective and high-performing EEG cleaning, is easy to use, and freely available on GitHub. Significance: We recommend RELAX for data cleaning across EEG studies to reduce artifact confounds, improve outcome measurement and improve inter-study consistency.