Bias-correction subspace projection method for background noise suppression in OPM-MEG systems
Fudan Zhao1, Haifeng Zhang2, Fengwen Zhao3
1School of Electronic Information Engineering, Beihang University, Beijing, 100191, China; Institute of Large-Scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Beijing, 100083, China.
Neuroimage
|July 14, 2026
Summary
A new bias-correction subspace projection (BCSP) method improves noise suppression for wearable Magnetoencephalography (MEG) using optically pumped magnetometers (OPMs). This technique enhances signal quality for clearer brain imaging in neuroscience and clinical applications.
Area of Science:
- Neuroimaging
- Biophysics
- Signal Processing
Background:
- Optically pumped magnetometers (OPMs) enable wearable Magnetoencephalography (MEG) with flexible sensor placement.
- Wearable OPM-MEG requires advanced environmental noise suppression due to sensor proximity to neural sources.
- Conventional signal space projection (SSP) for noise reduction can be biased by sensor noise and environmental interference.
Purpose of the Study:
- To develop a novel method for accurate noise subspace estimation in OPM-MEG.
- To address the bias issue in conventional SSP methods for improved noise removal.
- To enhance the signal-to-noise ratio (SNR) and preserve neural signal integrity in OPM-MEG.
Main Methods:
- Proposed a bias-correction subspace projection (BCSP) method incorporating a sensor noise and magnetic field coupling model.
- Utilized least-squares optimization to estimate and correct for subspace estimation bias.
- Implemented an eigenvector correction strategy using nonlinear soft thresholding for adaptive bias reduction.
Main Results:
- BCSP demonstrated superior noise suppression compared to conventional SSP in simulations and experiments.
- The method significantly improved the signal-to-noise ratio (SNR) in OPM-MEG recordings.
- Auditory evoked field experiments showed preserved N100 component waveform and highly focal cortical activation maps.
Conclusions:
- The BCSP method offers a robust approach to noise suppression in OPM-MEG.
- This technique enhances the reliability and focality of neuroimaging data.
- BCSP holds significant potential for advancing OPM-MEG applications in clinical medicine and neuroscience research.
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