# A wearable high-density MEG system with uOPMs

> **NIH NIH R43** · FIELDLINE, INC. · 2020 · $204,323

## Abstract

Abstract
This Phase I project will focus on developing key elements needed to achieve a wearable, high-density,
magnetoencephalography (MEG) device based on optically-pumped atomic magnetometers (OPMs).
OPM sensors have progressed to be comparable in sensitivity to liquid-helium-cooled superconducting
sensors (SQUIDs) but without the complexity and bulk required by cryogenic cooling. An OPM-based
MEG provides further advantages such as lower cost and the ability to place sensors directly on the
subject’s head. It has recently been shown that large, non-invasive “on-scalp” arrays of OPMs (1) will
outperform traditional fixed helmet MEG devices by a factor of 7.5 higher signal, and (2) may reach the
same resolution as invasive Electrocorticography (ECoG). However, there are a few advancements that
must be made to evolve from the proof-of-concept systems of a few tens of sensors to a practical full head
MEG system capable of producing high resolution images. In this project will address challenges like
cross-talk between sensors, background field compensation, and sensor calibration and localization. We
believe these innovations are necessary for a fully-integrated, wearable, high-density, and on-scalp MEG
device using ultra-sensitive OPMs.

## Key facts

- **NIH application ID:** 9986967
- **Project number:** 3R43MH118154-01A1S1
- **Recipient organization:** FIELDLINE, INC.
- **Principal Investigator:** Orang Alem
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $204,323
- **Award type:** 3
- **Project period:** 2019-09-01 → 2020-06-06

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9986967

## Citation

> US National Institutes of Health, RePORTER application 9986967, A wearable high-density MEG system with uOPMs (3R43MH118154-01A1S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9986967. Licensed CC0.

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