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Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

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Reference layer adaptive filtering (RLAF) for EEG artifact reduction in simultaneous EEG-fMRI.

David Steyrl¹, Gunther Krausz, Karl Koschutnig

¹Laboratory of Brain-Computer Interfaces, Institute of Neural Engineering, Graz University of Technology, Graz, Austria. BioTechMed-Graz, Graz, Austria.

Journal of Neural Engineering

|February 4, 2017

Summary

New methods using a reference layer cap and adaptive filtering significantly improve electroencephalography (EEG) data quality during simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) scans by reducing artifacts while preserving physiological signals.

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Area of Science:

Neuroimaging
Biomedical Engineering
Signal Processing

Background:

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) offers combined high temporal and spatial resolution for brain activity monitoring.
EEG data quality is severely compromised by artifacts generated by fMRI scanners, limiting its utility in combined recordings.
Existing artifact reduction methods require substantial improvement to effectively handle fMRI-induced noise.

Purpose of the Study:

To introduce and evaluate novel methods for substantially improving EEG data quality during simultaneous EEG-fMRI.
To develop artifact reduction techniques that preserve crucial physiological EEG components.
To assess the effectiveness of new algorithms in enhancing the quality of visual evoked potentials (VEP).

Main Methods:

Development of a reusable reference layer EEG cap prototype.
Implementation of reference layer adaptive filtering (RLAF) for artifact subtraction using reference layer data.
Introduction of multi-band reference layer adaptive filtering (MBRLAF) applied to bandwidth-limited EEG and reference channels.

Main Results:

RLAF outperformed baseline and previous reference layer artifact subtraction methods, especially at lower frequencies (<35 Hz).
MBRLAF, though computationally intensive, demonstrated high effectiveness across all EEG frequency ranges.
Both RLAF and MBRLAF significantly improved visual evoked potential (VEP) quality, evidenced by reduced trial-to-trial variability and enhanced classification accuracy, with MBRLAF showing superior performance.

Conclusions:

RLAF and MBRLAF represent highly effective strategies for mitigating fMRI-induced artifacts in simultaneous EEG recordings.
The developed methods successfully preserve essential physiological EEG signals, such as occipital alpha power and VEPs.
The reusable reference layer cap and adaptive filtering algorithms offer a promising advancement for high-quality simultaneous EEG-fMRI research.