Modular Arrays for High Precision Wearable MEG
Optically pumped magnetometers (OPMs) can be used for magnetoencephalography (MEG) with equivalent or improved signal to noise ratio, relative to cryogenic MEG, when sensors are placed close to the scalp. OPM-based MEG can also be used in mobile contexts if sensors are placed in lightweight, wearable arrays. Individually tailored, rigid helmets known as scannercasts are currently the only method…
Optically pumped magnetometers (OPMs) have the potential to deliver magnetoencephalography (MEG) signals with signal-to-noise ratios equivalent to or exceeding those of cryogenic MEG, when sensors are positioned near the scalp. OPM-based MEG can also be employed in mobile settings by incorporating lightweight, wearable sensor arrays.
Currently, personalized, rigid helmets called scannercasts remain the sole viable method for achieving high-precision on-scalp recordings during mobile MEG. However, scannercasts are costly to manufacture, necessitate pre-experiment structural imaging, and cause significant downtime during sensor transfers between scannercasts. This report introduces a solution to these challenges that preserves the benefits of scannercasts.
The authors outline a step-by-step process for building a modular, cap-based design applicable to all head sizes. Through simulations, they compare the leadfield power of their proposed array to an idealized array and a commercially available mobile solution. Additionally, they empirically validate their approach in five participants and provide a comprehensive data preparation and analysis pipeline.
By expanding the accessibility of OPM-based MEG, this design simultaneously enhances participant throughput to levels comparable with other imaging modalities. Most importantly, it eliminates the trade-off between signal quality, mobility, and practicality, effectively showcasing the distinct potential of OPM-based MEG as a tool for studying naturalistic behavior and clinical assessment with high precision.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.