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<p>The development of magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) has accelerated the evolution of brain imaging toward portable and movable configurations. Source localization using OPM-MEG plays a crucial role in both neuroscience and clinical applications. However, few studies have systematically evaluated localization performance under realistic measurement conditions that account for potential sensor errors. In this study, we simulated empirical OPM-MEG data with a biologically plausible model of ongoing brain activity to investigate the effects of calibration errors, specifically gain error, crosstalk, and angular misalignment, on the localization performance of three OPM array configurations: single-axis, dual-axis, and tri-axis sensors. We also compared four source inversion algorithms: Multiple Sparse Priors (MSP), Empirical Bayesian Beamformer (EBB), Minimum Norm (IID), and LORETA (COH). Our results indicate that gain error had minimal impact on localization accuracy, whereas crosstalk and angular error significantly degraded performance. These findings underscore the importance of OPM array calibration using reference magnetic fields, particularly for dual- and tri-axis configurations. Among the inversion methods tested, MSP demonstrated relative robustness to calibration errors and achieved the best overall performance, making it the most recommended approach. Furthermore, analysis of the relationship between cortical anatomy and localization accuracy revealed that deep neural sources are more susceptible to gain errors and thus require particular attention. To achieve accuracy comparable to an ideal OPM-MEG system, crosstalk should be kept below 2% and angular error under 2°.</p>

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Effects of Inverse Solutions and Calibration Errors on Localization Performance of OPM-MEG

  • 期刊论文

  • 发表日期：

    2026年4月15日

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  • Shengjie Qi,
  • Shuhao Cui,
  • Le Jia,
  • Tengyue Long,
  • Xinda Song,
  • Xiaolin Ning

摘要

The development of magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) has accelerated the evolution of brain imaging toward portable and movable configurations. Source localization using OPM-MEG plays a crucial role in both neuroscience and clinical applications. However, few studies have systematically evaluated localization performance under realistic measurement conditions that account for potential sensor errors. In this study, we simulated empirical OPM-MEG data with a biologically plausible model of ongoing brain activity to investigate the effects of calibration errors, specifically gain error, crosstalk, and angular misalignment, on the localization performance of three OPM array configurations: single-axis, dual-axis, and tri-axis sensors. We also compared four source inversion algorithms: Multiple Sparse Priors (MSP), Empirical Bayesian Beamformer (EBB), Minimum Norm (IID), and LORETA (COH). Our results indicate that gain error had minimal impact on localization accuracy, whereas crosstalk and angular error significantly degraded performance. These findings underscore the importance of OPM array calibration using reference magnetic fields, particularly for dual- and tri-axis configurations. Among the inversion methods tested, MSP demonstrated relative robustness to calibration errors and achieved the best overall performance, making it the most recommended approach. Furthermore, analysis of the relationship between cortical anatomy and localization accuracy revealed that deep neural sources are more susceptible to gain errors and thus require particular attention. To achieve accuracy comparable to an ideal OPM-MEG system, crosstalk should be kept below 2% and angular error under 2°.