Determining sensor geometry and gain in a wearable MEG system

• Published in: arXiv.org
• Main Authors: Hill, Ryan M, Gonzalo Reina Rivero, Tyler, Ashley J, Schofield, Holly, Doyle, Cody, Osborne, James, Bobela, David, Rier, Lukas, Gibson, Joseph, Tanner, Zoe, Boto, Elena, Bowtell, Richard, Brookes, Matthew J, Shah, Vishal, Holmes, Niall
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 11.10.2024
• Subjects: Beamforming; Calibration; Coils; Complexity; Dipoles; Magnetic fields; Magnetoencephalography; Sensor arrays; Sensors; Signal to noise ratio
• ISSN: 2331-8422

Optically pumped magnetometers (OPMs) are compact and lightweight sensors that can measure magnetic fields generated by current flow in neuronal assemblies in the brain. Such sensors enable construction of magnetoencephalography (MEG) instrumentation, with significant advantages over conventional MEG devices including adaptability to head size, enhanced movement tolerance, lower complexity and improved data quality. However, realising the potential of OPMs depends on our ability to perform system calibration, which means finding sensor locations, orientations, and the relationship between the sensor output and magnetic field (termed sensor gain). Such calibration is complex in OPMMEG since, for example, OPM placement can change from subject to subject (unlike in conventional MEG where sensor locations or orientations are fixed). Here, we present two methods for calibration, both based on generating well-characterised magnetic fields across a sensor array. Our first device (the HALO) is a head mounted system that generates dipole like fields from a set of coils. Our second (the matrix coil (MC)) generates fields using coils embedded in the walls of a magnetically shielded room. Our results show that both methods offer an accurate means to calibrate an OPM array (e.g. sensor locations within 2 mm of the ground truth) and that the calibrations produced by the two methods agree strongly with each other. When applied to data from human MEG experiments, both methods offer improved signal to noise ratio after beamforming suggesting that they give calibration parameters closer to the ground truth than factory settings and presumed physical sensor coordinates and orientations. Both techniques are practical and easy to integrate into real world MEG applications. This advances the field significantly closer to the routine use of OPMs for MEG recording.