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Related Experiment Video

Updated: Aug 9, 2025

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

Published on: April 16, 2010

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

N W Bailey¹, A T Hill², M Biabani³

¹Central Clinical School Department of Psychiatry, Monash University, Camberwell, VIC, 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

Summary

The RELAX pipeline effectively cleans electroencephalography (EEG) artifacts for Event-Related Potentials (ERPs), even with low-frequency filtering. This automated method improves data consistency and reliability in ERP research.

Keywords:

Artifact reduction Blinks Electroencephalography Event-related potentials Muscle Pre-processing

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Last Updated: Aug 9, 2025

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

Published on: April 16, 2010

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

Area of Science:

Neuroscience
Signal Processing

Background:

Electroencephalography (EEG) records neural activity, often analyzed as Event-Related Potentials (ERPs).
Non-neural artifacts contaminate EEG data, complicating ERP analysis and requiring artifact cleaning.
Existing automated artifact cleaning methods often exclude low frequencies vital for ERPs (<1 Hz).

Purpose of the Study:

To evaluate the RELAX (Reduction of Electroencephalographic Artifacts) pipeline for cleaning EEG data intended for ERP analysis.
To assess RELAX's performance compared to established artifact cleaning pipelines.

Main Methods:

Compared multiple RELAX pipeline versions against four standard EEG cleaning pipelines.
Evaluated cleaning performance using artifact cleaning metrics and ERP condition-based variance.
Utilized a Go-Nogo task dataset for comparative analysis.

Main Results:

RELAX, particularly with Multi-channel Wiener Filtering (MWF) and wavelet-enhanced independent component analysis (wICA_ICLabel), demonstrated superior artifact cleaning.
These RELAX configurations yielded highly dependable ERP estimates.
RELAX maintained high cleaning performance even with data high-pass filtered at 0.25 Hz, suitable for ERPs.

Conclusions:

RELAX offers effective artifact cleaning for ERP research, preserving crucial low-frequency information.
The pipeline is user-friendly, implemented in MATLAB via EEGLAB, and available on GitHub.
RELAX is recommended for enhancing objectivity and consistency in ERP studies.