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Modelling optically pumped magnetometer interference in MEG as a spatially homogeneous magnetic field
Tim M Tierney1, Nicholas Alexander1, Stephanie Mellor1
1Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, 12 Queen Square, London WC1N 3AR, UK.
Neuroimage
|August 21, 2021
Summary
This study introduces a simple method to reduce magnetic interference in magnetoencephalography (MEG) experiments using optically pumped magnetometers. Removing a homogeneous magnetic field significantly improves signal quality and statistical power for brain imaging.
Area of Science:
- Biophysics
- Neuroscience
- Biomedical Engineering
Background:
- Optically pumped magnetometers (OPMs) offer high sensitivity for magnetoencephalography (MEG).
- Magnetic interference poses a significant challenge in OPM-based MEG, potentially obscuring neural signals.
- Current methods for interference reduction can be complex or risk removing neural data.
Purpose of the Study:
- To propose and validate a simplified model for magnetic interference in OPM-MEG.
- To demonstrate that modeling interference as a homogeneous magnetic field improves data quality.
- To assess the risk of removing neural signals with this method.
Main Methods:
- Modeling magnetic interference as a spatially homogeneous magnetic field.
- Utilizing sensor orientation information for the model.
- Developing a framework to assess the risk of signal removal.
- Validating the method with a binaural auditory evoked response paradigm.
Main Results:
- The homogeneous magnetic field model effectively reduces sensor-level variance.
- Statistical power is substantially improved by applying this correction.
- Sensor-level signal-to-noise ratio (SNR) increased by a factor of 3.
- Multi-axis recordings were found to reduce the risk of removing neural signals.
Conclusions:
- A homogeneous magnetic field correction is a simple yet powerful preprocessing step for OPM-MEG.
- This method enhances data quality without requiring detailed neuroanatomy or sensor position information.
- The approach offers a low risk of removing neural signals, particularly with multi-axis recordings.
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