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

N W Bailey1, M Biabani2, A T Hill3

  • 1Central 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.

Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology
|February 23, 2023
PubMed
Summary
This summary is machine-generated.

RELAX, a new automated pipeline, effectively cleans electroencephalographic (EEG) data by removing artifacts. This tool enhances measurement accuracy and consistency in EEG studies.

Keywords:
Artifact reductionBlinksElectroencephalographyMuscleNeural oscillationsPre-processing

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Area of Science:

  • Neuroscience
  • Signal Processing

Background:

  • Electroencephalographic (EEG) data frequently contain non-neural artifacts.
  • Manual artifact cleaning is time-consuming and costly.
  • Automated solutions are needed to improve EEG data quality.

Purpose of the Study:

  • To develop a fully automated EEG cleaning pipeline, named RELAX (Reduction of Electroencephalographic Artifacts).
  • To address all major artifact types in EEG data.
  • To improve the measurement of EEG outcomes.

Main Methods:

  • RELAX utilizes Multi-channel Wiener filtering (MWF) and/or wavelet enhanced independent component analysis (wICA).
  • Artifacts are identified using ICLabel for wICA (wICA_ICLabel).
  • Performance was evaluated on three datasets against six existing pipelines using various artifact cleaning metrics.

Main Results:

  • RELAX, particularly with MWF and wICA_ICLabel, demonstrated superior performance in removing blink and muscle artifacts while preserving neural signals.
  • The wICA_ICLabel-only version showed potential for better differentiation of alpha oscillations in cognitive tasks.
  • RELAX achieved high performance in artifact reduction and signal preservation.

Conclusions:

  • RELAX offers an automated, objective, and high-performing solution for EEG data cleaning.
  • The pipeline is user-friendly and publicly available.
  • RELAX is recommended for reducing artifact confounds and enhancing inter-study consistency in EEG research.