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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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Optimizing EEG ICA decomposition with data cleaning in stationary and mobile experiments.

M Klug^1,2, T Berg³, K Gramann³

¹Young Investigator Group Intuitive XR, Neuroadaptive Human-Computer Interaction, Institute of Medical Technology, BTU Cottbus-Senftenberg, Cottbus, Germany. marius.klug@b-tu.de.

Scientific Reports

|June 19, 2024

Summary

Independent Component Analysis (ICA) effectively removes artifacts in electroencephalography (EEG) data, even with high motion intensity. Moderate data cleaning improves EEG signal decomposition, making AMICA robust for mobile studies.

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Area of Science:

Neuroscience
Signal Processing
Biomedical Engineering

Background:

Mobile electroencephalography (EEG) protocols increase data artifacts.
Independent Component Analysis (ICA) is crucial for artifact removal in EEG.
Pre-decomposition artifact removal, like sample rejection, is standard practice.

Purpose of the Study:

To systematically evaluate the impact of motion intensity and automatic sample rejection strength on AMICA decomposition quality.
To determine the optimal level of data cleaning for robust EEG artifact removal.
To assess the robustness of the AMICA algorithm under varying artifactual conditions.

Main Methods:

Conducted AMICA decompositions on eight open-access EEG datasets.
Varied motion intensity and sample rejection criteria across datasets.
Evaluated decomposition quality using metrics like mutual information, component proportions, residual variance, and signal-to-noise ratio.

Main Results:

Increased motion intensity significantly degraded decomposition quality within individual studies.
Stronger data cleaning improved decomposition, but less than anticipated.
AMICA demonstrated robustness, performing well even with minimal data cleaning.

Conclusions:

Moderate data cleaning (5-10 AMICA sample rejection iterations) generally enhances EEG decomposition.
The AMICA algorithm is robust to varying levels of motion artifact and cleaning intensity.
Findings support the use of AMICA for artifact removal in mobile EEG studies with varying data quality.