The advent of optically pumped magnetometers (OPMs) facilitates the development of on-scalp magnetoencephalography (MEG). In particular, the triaxial OPM emerged recently, making simultaneous measurements of all three orthogonal components of vector fields possible. The detection of triaxial magnetic fields improves the interference suppression capability and achieves higher source localization accuracy using fewer sensors. The source localization accuracy of MEG is based on the accurate co-registration of MEG and MRI. In this study, we proposed a triaxial co-registration method according to combined principal component analysis and iterative closest point algorithms for use of a flexible cap. A reference phantom with known sensor positions and orientations was designed and constructed to evaluate the accuracy of the proposed method. Experiments showed that the average co-registered position errors of all sensors were approximately 1 mm and average orientation errors were less than 2.5° in the X - and Y orientations and less than 1.6° in the Z orientation. Furthermore, we assessed the influence of co-registration errors on the source localization using simulations. The average source localization error of approximately 1 mm reflects the effectiveness of the co-registration method. The proposed co-registration method facilitates future applications of triaxial sensors on flexible caps.

Objective: Magnetoencephalography (MEG) is a non-invasive technique that measures the magnetic fields of brain activity. In particular, a new type of optically pumped magnetometer (OPM)-based wearable MEG system has been developed in recent years. Source localization in MEG can provide signals and locations of brain activity. However, conventional source localization methods face the difficulty of accurately estimating multiple sources. The present study presented a new parametric method to estimate the number of sources and localize multiple sources. In addition, we applied the proposed method to a constructed wearable OPM-MEG system. Methods: We used spatial clustering of the dipole spatial distribution to detect sources. The spatial distribution of dipoles was obtained by segmenting the MEG data temporally into slices and then estimating the parameters of the dipoles on each data slice using the particle swarm optimization algorithm. Spatial clustering was performed using the spatial-temporal density-based spatial clustering of applications with a noise algorithm. The performance of our approach for detecting multiple sources was compared with that of four typical benchmark algorithms using the OPM-MEG sensor configuration. Results: The simulation results showed that the proposed method had the best performance for detecting multiple sources. Moreover, the effectiveness of the method was verified by a multimodel sensory stimuli experiment on a real constructed 31-channel OPM-MEG. Conclusion: Our study provides an effective method for the detection of multiple sources. Significance: With the improvement of the source localization methods, MEG may have a wider range of applications in neuroscience and clinical research.

Magnetoencephalography (MEG) is a powerful noninvasive neuroimaging technique with high spatiotemporal resolution. Recently, a new generation of MEG, optically pumped magnetometer (OPM)-based wearable MEG, has been developed. Compared to the conventional MEG based on superconducting quantum interference devices with a fixed sensor array, the OPM-MEG is closer to the scalp and is movement-compatible. This provides an excellent opportunity for MEG to expand its further application scenarios, such as virtual reality interactive tasks and the scanning of participants with movement disorders. In this study, we reviewed the hardware composition and performed artifact analysis of wearable OPM-MEGs. The hurdle for the OPM-MEG in achieving movement compatibility is its susceptibility to large head-movement artifacts, which is related to the magnitude and gradient levels of the background magnetic fields. Therefore, we analyzed hardware and software methods to suppress movement artifacts. In addition, we summarize the performed experiments and applications involving the participants’ movements to illustrate the current movement-compatible performance of the OPM-MEG. Finally, we discussed the implications, challenges and future research priorities of improving movement compatibility. This study aims to provide a reference for researchers who attempt to use and apply wearable OPM-MEG in movement-related neuroscience or clinical research.

Optically pumped magnetometer (OPM)-based magnetoencephalography (MEG) has attracted significant attention due to its flexible sensor configuration and closer proximity to the scalp. The source localization technique of MEG is used in neuroscience and clinical research. Different head models have been developed to provide a forward solution for MEG source localization. However, the effects of different head models have not yet been studied under the new OPM-MEG measurement scenario. In this simulation study, we investigated the effects of eight head models [a local sphere (LS), a corrected sphere (CS), a boundary element method (BEM)-based, and five finite-element method (FEM)-based models, including isotropic three-, four-, and five-compartment and two anisotropic five-compartment head models] for forward and inverse solutions. In addition, we studied the effects of these eight head models in a more realistic environment, such as under sensor coregistration error and measurement noise. The results showed that the source localization error (LE) between the simplified head model and the isotropic five-compartment FEM model can reach 4–5 mm, emphasizing the necessity of using an elaborate head model. In addition, in the comparison of forward and inverse problems, the distinction of cerebrospinal fluid (CSF) showed the most obvious improvement. Thus, an FEM head model with a CSF compartment is recommended. Furthermore, a high level of measurement noise (>80%, the ratio of root mean square of the noise matrix to the data matrix) and high coregistration errors (rotation error > 2°) significantly reduced the contributions of realistic head models.

Magnetoencephalography (MEG) is a noninvasive brain imaging technique that investigates dynamic brain changes by measuring magnetic fields generated by neural activities. Recently, significant advancements have been made in Optically Pumped Magnetometer-based Magnetoencephalography (OPM-MEG). Compared with conventional superconducting quantum interference device-based MEG, OPM-MEGs operate at room temperature, reducing costs and allowing more flexible sensor configurations. Most importantly, OPM-MEG allows for meas-urements during natural head movements, facilitating more nat-uralistic experiments and broader applications, including studies involving motor tasks and populations, such as young children or patients with movement disorders. However, motion-compatible OPM-MEGs face significant challenges because of the severe impact of motion artifacts on data quality. This study introduced a Dynamic Background Field Adaptive Filtering method (DBFAF) to suppress motion artifacts in OPM-MEG. The OPM-MEG data were processed by adaptively filtering out the motion artifacts measured using multiple orthogonally oriented OPM reference sensors fixed to a helmet. The simulation results demonstrate that the DBFAF method significantly improves data quality by effectively mitigating the impact of motion artifacts, surpassing the widely used moving-window regression methods. The efficacy and reliability of this method were further verified using median nerve stimulation in OPM-MEG experiments. This approach offers a novel solution for applying OPM-MEG in contexts involving participants’ head movements.