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 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.

Alterations in EEG functional connectivity in individuals with depression: A systematic review.

The brain works as an organised, network-like structure of functionally interconnected regions. Disruptions to interconnectivity in certain networks have been linked to symptoms of depression and impairments in cognition. Electroencephalography (EEG) is a low-burden tool by which differences in functional connectivity (FC) can be assessed.

Electroencephalographic Connectivity: A Fundamental Guide and Checklist for Optimal Study Design and Evaluation.

Brain connectivity can be estimated through many analyses applied to electroencephalography (EEG) data. However, substantial heterogeneity in the implementation of connectivity methods exists. Heterogeneity in conceptualization of connectivity measures, data collection, or data preprocessing may be associated with variability in robustness of measurement.