From Shielded Labs to Everyday Settings: Engineering a Portable OPM-MEG for Next-Generation Brain Monitoring

Magnetoencephalography (MEG) is a technology capable of imaging brain activity by measuring the magnetic fields produced by synchronized neuronal currents. Unlike EEG, in which signals can be distorted by the skull and scalp, MEG is able to capture brain activity with minimal interference, allowing it to accurately pinpoint the location of brain activity with remarkable precision (Brainbox Neuro, n.d.). In contrast, fMRI and fNIRS infer brain activity indirectly by measuring changes in blood oxygenation that take place several seconds after the underlying brain activity occurs (Brookes et al., 2022).

MEG’s combination of both high temporal and spatial resolution has made it an informative technique for studying brain function. It has made it possible for researchers to have a more detailed understanding of the neuronal processes that depend on communication between different cortical regions, like perception, language, and motor planning. In clinical practice, MEG has the ability to localize epileptic seizure foci and can aid in surgical planning and decisions regarding placements of intracranial electrodes (Hari & Salmelin, 2012). MEG has also provided crucial insights into the fundamental changes in functional connectivity that underlie clinical symptoms and cognitive defects such as schizophrenia, dementia, movement disorders, neurodevelopmental disorders, and epilepsy (Brickwedde et al., 2024).

A portable, wearable MEG system would allow researchers to measure the brain’s oscillatory patterns during real-world tasks that can be affected by cognitive and clinical disorders like movement, communication, and emotion. Disruptions in gamma-band synchronization are found in patients with schizophrenia, and gamma-band ranges are impaired in patients with psychosis (Brickwedde et al., 2024). With a portable MEG system, oscillatory defects like these could be tracked longitudinally, allowing researchers to detect functional connectivity dysfunctions before structural changes occur. In developmental research, a portable MEG system could provide more detailed insights into how neuronal connectivity dynamics evolve through different stages of growth and learning.

Because MEG and even current OPM-MEG technologies remain lab-bound, many research and clinical applications are not yet possible. Cognitive studies exploring brain activity during movement, communication, or emotional expression cannot be conducted because participants must remain still in a shielded room. This also makes scanning patients with involuntary movements, cognitive impairments, or anxiety particularly challenging, and until MEG can operate reliably outside of the lab, these opportunities for studying and treating brain network dysfunction in real-world settings are out of reach.

Although OPM-MEG systems have eliminated MEG’s reliance on cryogenically cooled superconducting quantum interference devices (SQUIDs), multiple engineering and practical barriers continue to confine OPM-MEG technology to lab environments. The primary issue is magnetic interference. OPM-MEG sensors are capable of detecting magnetic fields produced by neuronal currents, which are around one billion times smaller than the Earth’s magnetic field, and are drastically diminished by the magnetic fields of surrounding lab equipment and infrastructure. To preserve signal quality, OPM-MEG must operate in magnetically shielded rooms or use field-nulling coils that cancel external magnetic noise. While these methods are able to successfully isolate magnetic fields produced by neuronal currents, they are expensive and immobile, keeping the technology lab-bound (Bonnet et al., 2025).

While individual OPM sensors are small and lightweight, their cables and mounting structures are not advanced enough for unrestricted movement. While the OPM-MEG breakthrough has allowed the technology to move with the head, small sensor movements can change the baseline magnetic field and introduce consequential artifacts that can exceed neural signals of interest (Seymour et al., 2021). Because OPM-MEG sensors operate within a narrow range, even small movements through the Earth’s magnetic field can distort the sensors’ sensitivity to magnetic fluctuations (Boto et al., 2018).

The helmets and support frames that are needed to maintain consistent sensor orientation are bulky, especially in full-head arrays. The way sensors are organized and mounted impacts both patient comfortability and the technology’s performance. 3D-printed helmets designed from MRI scans offer superior accuracy by keeping sensors fixed in relation to individual differences in the scalp, but they are custom-made and costly to produce per patient. Generic 3D-printed helmets could provide a more practical compromise, but they still require precise alignment and calibration for every new user which increases setup time. The size and weight of OPM-MEG helmets also limits researchers’ ability to image babies’ brains (Brookes et al., 2022).

The power requirements of OPM-MEG sensors further restrict real-world use of the technology. Each sensor contains an atomic vapor, a laser, and a set of electromagnetic coils that control the magnetic field within the system. These components consume substantial amounts of power and generate heat, and as systems move towards higher numbers of sensors, a greater amount of power and heat will need to be dissipated. In current systems, insulation keeps the temperature on the scalp comfortable. Future OPM-MEG would require active cooling technology (Brookes et al., 2022). Because of these power demands, current OPM-MEG systems rely on tethered power supplies rather than being battery powered, making them impractical for mobile or bedside use.

Cost is also a significant barrier. Even OPM-MEG systems can cost several hundred thousand dollars, while full-head systems with magnetic shielding exceed one million dollars (Constantin, 2024). System maintenance, software licensing, and continuing calibration would also increase expenses, limiting access to MEG technology to well-funded research institutions. When ranked, the biggest obstacle for portability is environmental magnetic interference, then motion-related sensor instability, followed by setup complexity, power demands and thermal effects, and cost.

My goal for this design is to take the advantages MEG technology has, like high spatial and temporal resolution, and make it usable outside of a magnetically shielded lab. The reason OPM-MEG is still lab-bound despite the fact that the sensors themselves are lightweight is because the rest of the system still relies on bulky shielding, power, and calibration setup time. A truly portable OPM-MEG headset would need to miniaturize every aspect of the technology. The magnetic shielding, field-nulling coils, and sensor housing would need to be packed into a wearable headset, and the weaknesses the technology faces, like sensor-motion, magnetic interference, and calibration, would need to be fixed while aiming to maintain almost all of the MEG technology’s abilities.

Traditional OPM-MEG systems need thick mu-metal rooms to block environmental magnetic noise, one of the main reasons the technology is lab-bound (Bonnet et al., 2025). A portable design could use a magnetic shielding system consisting of mu-metal and graphene composites, which are efficient electromagnetic absorbers (Barani et al., 2020), reducing magnetic noise at the scalp, and field-nulling coils being placed around the sensor array would further cancel external magnetic noise. The coils continuously measure and cancel the external magnetic field fluctuations using feedback from reference OPM sensors that feed data back to the coils, which then generate compensatory magnetic fields to cancel out interference enough to make high-quality recordings possible outside of a shielded room in real time (Bonnet et al., 2025). It would not completely shield the OPM-MEG sensors from external magnetic interference, but it would be enough to make quality neuronal activity images outside of a shielded room.

When the head moves, small sensor shifts can distort the baseline magnetic field and mask magnetic fields produced by neuronal currents. Flexible “EEG-like” caps cause drops in the technology’s performance quality because the sensors move relative to the scalp, while 3D-printed helmets solve this problem, but are expensive and do not account for individual differences (Brookes et al., 2022). A solution could be to take the middle ground and design a semi-rigid, adjustable headset that is able to lock sensors in place while adapting to different head shapes. Adding small inertial sensors (like those used in VR headsets) could detect head movement in OPM-MEG sensors in real time (Borowska-Terka & Strumiłło, 2023). If used alongside a motion-tracking algorithm (Boto et al., 2018), it could allow the portable OPM-MEG system to correct motion-induced artifacts in the recorded signal. This way, the headset could tolerate movement without corrupting data.

Each individual OPM-MEG sensor requires optical pumping, which is a small laser that heats alkali gas to 150°C so that it is able to detect magnetic fields. This process uses a lot of power and generates heat that affects the sensors’ sensitivity while also being uncomfortable on the scalp (Bonnet et al., 2025). To combat this problem, I would propose the use of vertical-cavity surface-emitting lasers (VCSELs), which run at much lower frequencies, needing less power, and are much cooler than other laser systems (Affolderbach et al., 2000), combined with Helium-based OPM-MEG sensors that operate at room temperature with negligible heat dissipation, and thus do not need thermal insulation (Bonnet et al., 2025).

Rather than needing to calibrate the headsets with each use, a portable OPM-MEG system could integrate an automated calibration framework like the head-mounted array localization and orientation (HALO) system, which uses controlled magnetic coils mounted around the sensor array to generate known reference magnetic fields and automatically determine each sensor’s position (Hill et al., 2025). Incorporating HALO technology into a portable OPM-MEG system would allow for self-calibration and reduce reliance on technical staff.

The final design would be a lightweight, wireless headset that would transmit data wirelessly to an external processor. Although temporal resolution would be preserved, the spatial resolution would be expected to be slightly lower than that of a cryogenic MEG system. The combination of improved mobility, self-calibration, and thermal safety could make OPM-MEG a truly portable brain imaging device.

The most significant technical challenge for portable OPM-MEG systems is achieving a high signal-to-noise ratio in environments that are contaminated by magnetic interference. Neural magnetic fields are on the order of femtotesla, while the Earth’s magnetic field is millions of times stronger. To address this, the mu-metal (Bonnet et al., 2025) and graphene composite layer reduces magnetic noise passively (Barani et al., 2020), while active field-nulling coils cancel out magnetic interference in real-time. These reference sensors continuously sample the ambient magnetic field and generate opposing magnetic fields (Bonnet et al., 2025). AI adaptive noise filtering in the data transmission could identify and remove non-biological magnetic patterns using machine learning models (Fernandez-de-Retana et al., 2025).

Small head movements can distort the baseline magnetic field, resulting in signal artifacts that are amplified due to the fact that OPM-MEG sensors detect absolute magnetic fields rather than measuring the differences, obscuring neural data (Seymour et al., 2021). A solution could be to address this by including inertial sensors (Borowska-Terka & Strumiłło, 2023) placed alongside OPM-MEG sensors to detect head orientation and movement in real time. These motion parameters could be fed into compensating motion-tracking algorithms that eliminate motion-related changes from the magnetic signal and preserve neural data (Boto et al., 2018). Speculatively, AI algorithms could further improve the accuracy of including these components by learning subject-specific signatures and adjusting algorithms individually per patient.

Each OPM-MEG sensor requires continuous optical pumping, which consists of heating alkali gas to 150°C. This demands substantial amounts of power and generates amounts of heat that can affect both sensor sensitivity and patient comfort (Bonnet et al., 2025). A solution could be to incorporate VCSELs that operate efficiently at low power and emit minimal amounts of heat (Affolderbach et al., 2000). Coupled with Helium-based OPM-MEG sensors that function at room temperature removes the necessity for active cooling techniques (Bonnet et al., 2025).

While magnetic interference, motion artifacts, and power consumption remain the most critical engineering challenges, they are becoming increasingly solvable through modern material science and artificial intelligence. The convergence of graphene composites, VCSEL optics, and AI correction systems puts portable OPM-MEG technology on the edge of real-world possibilities.

With portable OPM-MEG, researchers could precisely measure the brain’s oscillatory and connectivity patterns during natural movement or speech, which is impossible with conventional MEG. This would allow for longitudinal tracking of functional connectivity changes in disorders like schizophrenia, dementia, epilepsy, and Parkinson’s disease. Clinicians could monitor gamma-band synchronicity over time, which is a biomarker that is impaired in patients with schizophrenia and psychosis (Brickwedde et al., 2024). Portable OPM-MEG would also be able to image neuronal development in young children and improve early detection and diagnosis of developmental delays. Real-time neurofeedback would also be able to guide recovery following stroke or traumatic brain injury, allowing clinicians to visualize cortical reorganization after trauma as patients perform physical or cognitive exercises.

Based on OPM-MEG progress, a fully portable OPM-MEG system seems to be on the verge of existence. Helium-based OPM-MEG sensors that operate at room temperature (Bonnet et al., 2025), machine-learning correction algorithms (Fernandez-de-Retana et al., 2025), and lightweight graphene-based magnetic shielding materials (Barani et al., 2020) are all technical components that already exist. The remaining barriers are mainly engineering integration and cost reduction.

The initial market would likely include academic and clinical research centers studying neurological and psychiatric disorders. Hospitals could adopt the system for bedside monitoring of epileptic patients, while rehabilitations could use the technology for real-time feedback and training. Over time, as manufacturing scales and AI-driven calibration becomes more accessible and automated, versions could emerge for consumer-grade cognitive assessment or mental health tracking.

To verify that the portable OPM-MEG system produces equivalent data to lab-based MEG systems, validation would need to follow a multi-phase protocol. Simultaneous recordings could be made with both portable and lab-based MEG systems during standardized tasks to directly compare signal-to-noise ratio, and the precision of spatial and temporal resolutions. Cross-modal validation with fMRI or EEG could also confirm spatial and temporal correspondences of activation patterns. Longitudinal reliability testing could also assess signal consistency outside of magnetically shielded environments. Consistent data across the combination of these methods would demonstrate that a portable OPM-MEG system retains the validity of lab-based MEG technologies while enabling real-world applications.