Back to Search
Start Over
Real-time, model-based magnetic field correction for moving, wearable MEG.
- Source :
-
NeuroImage [Neuroimage] 2023 Sep; Vol. 278, pp. 120252. Date of Electronic Publication: 2023 Jul 11. - Publication Year :
- 2023
-
Abstract
- Most neuroimaging techniques require the participant to remain still for reliable recordings to be made. Optically pumped magnetometer (OPM) based magnetoencephalography (OP-MEG) however, is a neuroimaging technique which can be used to measure neural signals during large participant movement (approximately 1 m) within a magnetically shielded room (MSR) (Boto et al., 2018; Seymour et al., 2021). Nevertheless, environmental magnetic fields vary both spatially and temporally and OPMs can only operate within a limited magnetic field range, which constrains participant movement. Here we implement real-time updates to electromagnetic coils mounted on-board of the OPMs, to cancel out the changing background magnetic fields. The coil currents were chosen based on a continually updating harmonic model of the background magnetic field, effectively implementing homogeneous field correction (HFC) in real-time (Tierney et al., 2021). During a stationary, empty room recording, we show an improvement in very low frequency noise of 24 dB. In an auditory paradigm, during participant movement of up to 2 m within a magnetically shielded room, introduction of the real-time correction more than doubled the proportion of trials in which no sensor saturated recorded outside of a 50 cm radius from the optimally-shielded centre of the room. The main advantage of such model-based (rather than direct) feedback is that it could allow one to correct field components along unmeasured OPM axes, potentially mitigating sensor gain and calibration issues (Borna et al., 2022).<br />Competing Interests: Declaration of Competing Interest This work was partly funded by a Wellcome award which involves a collaboration agreement with QuSpin.<br /> (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1095-9572
- Volume :
- 278
- Database :
- MEDLINE
- Journal :
- NeuroImage
- Publication Type :
- Academic Journal
- Accession number :
- 37437702
- Full Text :
- https://doi.org/10.1016/j.neuroimage.2023.120252