Reference Layer Artefact Subtraction (RLAS): Electromagnetic Simulations

The utility of EEG-fMRI is limited by the large artefact voltages produced in EEG recordings made during concurrent fMRI. Novel approaches for reducing the magnitude and variability of the artefacts are, therefore, required. One such approach involves using an EEG cap incorporating a reference layer (RL), which has similar conductivity to biological tissue and is electrically isolated from the scalp. The RL carries a secondary set of electrodes and leads that precisely overlay on the scalp electrodes so that similar voltages are anticipated to induce at the RL and scalp electrodes in the presence of static and time-varying magnetic fields. RL artefact subtraction (RLAS), which involves taking the difference of the voltages at the two electrodes, should, therefore, attenuate artefacts while leaving neuronal voltages unaffected. Previous experimental work has demonstrated the potential of the RLAS system in removing different types of EEG artefact. However, to get the best performance from the RLAS system, it is important to verify the underlying assumptions of RLAS and to optimize the RLAS system based on electromagnetic simulations. In this paper, electromagnetic modeling was used to simulate the voltages induced in a hemispherical RL and a spherical volume conductor (VC) under the influence of a static magnetic field and time-varying magnetic field gradient. By evaluating the differences in the voltages produced in the RL and VC, as the RL geometry is varied, the efficacy of the RLAS approach is tested and an optimal RL design is identified. The simulations performed accounted for realistic rotations, shifts, nodding, and shaking of the RLAS system with respect to the gradient isocentre, thus giving insight into the optimum RLAS set-up and the potential for improved EEG cap design for an RLAS system.

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Published in: IEEE Access
License: https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/OAPA.html
See article on publisher's website: https://dx.doi.org/10.1109/access.2019.2892766