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Interference suppression techniques for OPM-based MEG: Opportunities and challenges.

Robert A Seymour1, Nicholas Alexander1, Stephanie Mellor1

  • 1Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK.

Neuroimage
|December 21, 2021
PubMed
Summary
This summary is machine-generated.

Optically pumped magnetometers (OPMs) enable wearable magnetoencephalography (MEG), but require advanced interference suppression. This paper reviews hardware and signal processing solutions for cleaner OPM-MEG data, offering practical examples for researchers.

Keywords:
BeamformerInterference suppressionMEGNoise reductionOPMSignal processing

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Area of Science:

  • Neuroimaging
  • Biophysics
  • Sensor Technology

Background:

  • Magnetoencephalography (MEG) faces challenges with low signal magnitude compared to interference.
  • Optically Pumped Magnetometers (OPMs) offer wearable MEG solutions, enabling new experimental possibilities.
  • OPMs present unique interference suppression challenges, particularly with mobile participants and integrated equipment.

Purpose of the Study:

  • To review hardware solutions for OPM interference suppression.
  • To outline signal processing strategies for enhancing neuromagnetic signals from OPM data.
  • To provide practical, tutorial-style examples of interference suppression pipelines for OPM-MEG.

Main Methods:

  • Review of existing hardware solutions for OPM interference suppression.
  • Description of signal processing techniques including regression-based, temporal, and spatial filtering.
  • Demonstration of interference suppression using worked-through OPM-MEG experimental data.

Main Results:

  • Identified various hardware approaches to mitigate OPM interference.
  • Detailed practical signal processing strategies applicable to OPM data.
  • Provided reproducible examples with data and code for OPM interference suppression.

Conclusions:

  • Effective interference suppression is crucial for the advancement of OPM-based MEG.
  • The reviewed methods and provided examples serve as a practical resource for researchers.
  • This work facilitates the broader adoption and application of OPM-MEG technology.