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

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.

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

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.

Differential plasticity of extensor and flexor motor cortex representations following visuomotor adaptation.

Representations within the primary motor cortex (M1) are capable of rapid functional changes following motor learning, known as use-dependent plasticity. GABAergic inhibition plays a role in use-dependent plasticity. Evidence suggests a different capacity for plasticity of distal and proximal upper limb muscle representations. However, it is unclear whether the motor cortical representations of forearm flexor and extensor muscles also have different capacities for plasticity. The current study used transcranial magnetic stimulation to investigate motor cortex excitability and inhibition of forearm flexor and extensor representations before and after performance of a visuomotor adaptation task that primarily targeted flexors and extensors separately. There was a decrease in extensor and flexor motor-evoked potential (MEP) amplitude after performing the extensor adaptation, but no change in flexor and extensor MEP amplitude after performing the flexor adaptation. There was also a decrease in motor cortical inhibition in the extensor following extensor adaptation, but no change in motor cortical inhibition in the flexor muscle following flexor adaptation or either of the non-prime mover muscles. Findings suggest that the forearm extensor motor cortical representation exhibits plastic change following adaptive motor learning, and broadly support the distinct neural control of forearm flexor and extensor muscles.