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Optimizing OPM-MEG Sensor Layouts Using the Sequential Selection Algorithm with Simulated Sources and Individual

Urban Marhl1,2, Rok Hren1,3, Tilmann Sander4

  • 1Institute of Mathematics, Physics and Mechanics, 1000 Ljubljana, Slovenia.

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|February 27, 2026
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Summary
This summary is machine-generated.

Optimizing sensor placement for magnetoencephalography (MEG) using optically pumped magnetometers (OPMs) improves spatial resolution. This simulation-driven method efficiently identifies optimal sensor positions for accurate brain activity mapping with fewer sensors.

Keywords:
auditory-evoked fieldsmagnetic field mapsmagnetoencephalographyoptically pumped magnetometerssensor optimizationsequential selection algorithm

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Area of Science:

  • Biophysics
  • Neuroscience
  • Biomedical Engineering

Background:

  • Optically pumped magnetometer (OPM)-based magnetoencephalography (MEG) offers enhanced signal-to-noise ratio due to closer sensor proximity to the scalp compared to traditional superconducting quantum interference device (SQUID) systems.
  • Optimal sensor placement is crucial for achieving high spatial resolution in OPM-MEG, particularly with a limited number of sensors.

Purpose of the Study:

  • To develop and validate a simulation-driven methodology for optimizing OPM-MEG sensor layouts using individual subject anatomical data.
  • To assess the efficiency and accuracy of the proposed optimization strategy in capturing neural information and reconstructing sources.

Main Methods:

  • Generation of realistic forward models using individual magnetic resonance imaging (MRI) derived head surfaces and simulated equivalent current dipoles (ECDs).
  • Application of the sequential selection algorithm (SSA) to simulated magnetic field map (MFM) databases to identify sensor positions maximizing information capture.
  • Benchmarking personalized layouts against full-head measurements and evaluating source localization accuracy for auditory evoked fields (AEFs).

Main Results:

  • Simulation-based SSA optimization effectively identifies sensor configurations that improve cortical region coverage and reduce the number of sensors needed for accurate source reconstruction.
  • Optimized sensor layouts demonstrated performance comparable to those derived from measured data, with the first 15-20 sensors capturing over 95% of the information.
  • Source localization of the M100 auditory response yielded errors below 5 mm, indicating high precision.

Conclusions:

  • The simulation-driven SSA approach provides an efficient and effective method for personalizing OPM-MEG sensor layouts without requiring extensive pre-existing measurement databases.
  • The optimization strategy is robust to individual head geometry variations, suggesting feasibility for clinical applications using representative anatomical models.