RELAX part 2: A fully automated EEG data cleaning algorithm that is applicable to Event-Related-Potentials. (May 2023)

• Record Type: Journal Article
• Authors: Bailey, N.W.; Hill, A.T.; Biabani, M.; Murphy, O.W.; Rogasch, N.C.; McQueen, B.; Miljevic, A.; Fitzgerald, P.B.
• Is Part Of: Clinical neurophysiology. Volume 149(2023)
• Page Start: 202
• Page End: 222
• Publication Date: 2023-05
• Subjects: Electroencephalography -- Event-related potentials -- Pre-processing -- Artifact reduction -- Blinks -- Muscle; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2023.01.018
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: RELAX is a fully automated free EEG cleaning pipeline with capability to clean data using 0.25 Hz high-pass filter settings for analysis of event-related potentials. RELAX provided high artifact reduction performance on data with filter settings appropriate for event-related potential research. ERP data cleaned with RELAX provided high reliability scores, and high variance explained by ERPs comparisons from a commonly used task. Abstract: Objective: Electroencephalography (EEG) is often used to examine neural activity time-locked to stimuli presentation, referred to as Event-Related Potentials (ERP). However, EEG is influenced by non-neural artifacts, which can confound ERP comparisons. Artifact cleaning reduces artifacts, but often requires time-consuming manual decisions. Most automated methods filter frequencies <1 Hz out of the data, so are not recommended for ERPs (which contain frequencies <1 Hz). Our aim was to test the RELAX (Reduction of Electroencephalographic Artifacts) pre-processing pipeline for use on ERP data. Methods: The cleaning performance of multiple versions of RELAX were compared to four commonly used EEG cleaning pipelines across both artifact cleaning metrics and the amount of variance in ERPs explained by different conditions in a Go-Nogo task. Results RELAX with Multi-channel Wiener Filtering (MWF) and wavelet-enhanced independent component analysis applied to artifacts identified with ICLabel (wICA_ICLabel) cleaned data most effectively and produced amongst the most dependable ERP estimates. RELAX with wICA_ICLabel only or MWF_only may detect effects better for some ERPs. Conclusions: RELAX shows high artifact cleaning performance even when data is high-pass filtered at 0.25 Hz (applicable to ERP analyses). Significance: RELAX is easy to implement via EEGLAB in MATLAB and freely available on GitHub. Given its performance and objectivity we recommend RELAX to improve artifact cleaning and consistency across ERP research.