Fuzhi Cao

Optically Pumped Magnetometers (OPMs) have emerged as a promising technology for developing flexible, wearable magnetoencephalography (OPM-MEG) systems, offering high spatiotemporal resolution without the need for cryogenic cooling. However, their application to phase–amplitude coupling (PAC)-based neural decoding remains largely unexplored. Investigating their decoding performance is essential for evaluating the capability of OPM-MEG in characterizing complex neural dynamics and discriminating motor states. In this study, OPM-MEG was utilized to record brain activity during rest, motor imagery, and motor execution tasks. A two-stage temporal optimization strategy combining time-resolved PAC localization and the Kullback–Leibler modulation index (KL-MI) was employed to extract robust PAC features from low-frequency phase and high-frequency amplitude coupling. α–γ and θ–γ PAC features were subsequently fed into a multiclass linear discriminant analysis (LDA) classifier for motor state decoding, and compared against baseline band-power feature decoding performance. Experimental results demonstrate that PAC features derived from OPM-MEG significantly outperform the corresponding baseline band-power features in decoding performance. Notably, α–γ PAC features effectively discriminate among different motor states, achieving a balanced accuracy of 85.91% in 10-fold cross-validation. This performance significantly exceeds the 50% one-vs-rest chance level and outperforms θ–γ PAC features. These findings provide initial evidence for the feasibility of OPM-MEG in PAC-based motor state decoding and a preliminary case study for characterizing motor-related neural dynamics in a wearable MEG system.

Optically pumped magnetometer-based magnetoencephalography (OPM-MEG), with its flexible sensor configuration and wide range of application scenarios, has become a powerful complement to conventional superconducting quantum interference device magnetoencephalography (SQUID-MEG). However, this higher flexibility also means that OPM-MEG sensor arrays are more susceptible to interference from complex and variable background magnetic noise. Previous research has shown that deploying reference sensors around the scalp array for noise cancellation is an effective strategy. Nonetheless, the selection of its key parameter, the spatial distance between the reference and scalp sensors, commonly termed the synthetic baseline, predominantly relies on empirical rules and lacks systematic theoretical optimization. To address this issue, this study thoroughly investigates the fundamental impact of the synthetic baseline on the system’s noise suppression performance. Simulation results demonstrate that the optimal baseline length is not a fixed value but varies systematically with environmental noise characteristics and the specific requirements of the source localization task. Building on this analysis, a Baseline Adaptive Reference Optimization (BARO) method is proposed. As an environment-driven strategy, the BARO method automatically determines the optimal baseline configuration by maximizing the output signal-to-noise ratio (SNR). Compared to traditional fixed-baseline configurations, the proposed BARO method significantly enhances the output SNR and effectively reduces the localization error of equivalent current dipoles within the brain across various simulated complex noise scenarios. This work provides a physically interpretable criterion for baseline optimization and offers theoretical support for environment-adaptive configuration of OPM-MEG sensor arrays.

Magnetoencephalography (MEG), a non-invasive neuroimaging technique with millisecond temporal resolution and millimeter spatial resolution, is an essential tool for investigating neurological disorders. This study conducted a systematic analysis of 4,040 relevant publications from the Web of Science database (2000–2024) using VOSviewer and CiteSpace to identify research hotspots and trends in MEG applications for neurological disorders over the past 24 years. The analysis revealed a steady annual increase in publications, and showed that the research evolved in three distinct phases: the early period (2000–2004) focused primarily on fundamental MEG principles, the intermediate period (2005–2015) shifted toward MEG signal analysis methods including network analysis, and the recent period (2016–2024) emphasized brain network functional connectivity analysis. Emerging research hotspots converged on the clinical application of analytical methods such as brain functional connectivity, encompassing areas such as the early diagnosis of Alzheimer’s disease and preoperative evaluation for epilepsy. This study provided the first comprehensive bibliometric analysis of research hotspots and developmental trends in MEG applications for neurological disorders. These findings provided researchers with a clear understanding of the field’s evolution and current landscape, thereby facilitating the rapid identification of promising research directions.

Brain–computer interfaces (BCIs) based on steady-state visual evoked responses (SSVERs) offer high information transfer rates and signal-to-noise ratios, making them widely used in applications, such as brain-controlled typing and wheelchair navigation. However, traditional SSVER decoding using spatial filtering methods requires substantial training data, limiting practical usability due to the time-consuming calibration process. To address this issue, we propose an exactly periodic component analysis (EPCA) method, which extracts exactly periodic signals using multiple orthogonal subspace projections and constructs spatial filters via generalized eigendecomposition. EPCA eliminates the need for multiple training trials while maintaining high decoding performance. We validated the effectiveness of EPCA on four publicly available datasets and one self-collected BCI dataset. Results demonstrate that EPCA significantly outperforms conventional spatial filtering methods, recent single-trial calibration approaches, and deep learning-based decoding models. Across all five datasets, EPCA consistently achieved the highest decoding performance under single-trial calibration. Furthermore, an online spelling experiment confirmed the real-world effectiveness of the proposed method. By reducing calibration requirements without compromising performance, EPCA greatly enhances the practicality of SSVER-based BCIs for real-world applications.

Accurate co-registration between on-scalp Optically Pumped Magnetometer (OPM)–Magnetoencephalography (MEG) sensors and anatomical Magnetic Resonance Imaging (MRI) remains a critical bottleneck restricting the spatial fidelity of source localization. Optical Scanning Image (OSI) methods can provide high spatial accuracy but depend on surface visibility and cannot directly determine the internal sensitive point of each OPM sensor. Coil-based magnetic dipole localization, in contrast, targets the sensor’s internal sensitive volume and is robust to occlusion, yet its accuracy is affected by coil fabrication imperfections and the validity of the dipole approximation. To integrate the complementary advantages of both approaches, we propose a hybrid co-registration framework that combines Rigid Coil Structures (RCS), magnetic dipole-based sensor localization, and optical orientation constraints. A complete multi-stage co-registration pipeline is established through a unified mathematical formulation, including MRI–OSI alignment, OSI–RCS transformation, and final RCS–sensor localization. Systematic simulations are conducted to evaluate the accuracy of the magnetic dipole approximation for both cylindrical helical coils and planar single-turn coils. The results quantify how wire diameter, coil radius, and turn number influence dipole model fidelity and offer practical guidelines for coil design. Experiments using 18 coils and 11 single-axis OPMs demonstrate positional accuracy of a few millimeters, and optical orientation priors suppress dipole-only orientation ambiguity in unstable channels. To improve the stability of sensor orientation estimation, optical scanning of surface markers is incorporated as a soft constraint, yielding substantial improvements for channels that exhibit unstable results under dipole-only optimization. Overall, the proposed hybrid framework demonstrates the feasibility of combining magnetic and optical information for robust OPM–MEG co-registration.

High-precision sensor co-registration is a critical prerequisite for achieving high-resolution imaging in Optically Pumped Magnetometer–Magnetoencephalography (OPM-MEG) systems. The conventional magnetic dipole fitting method, essentially a multipole expansion approximation of a finite-size coil, exhibits accuracy that strongly depends on spatial geometric factors such as coil–sensor distance, dipole orientation, and the projection angle of the sensor’s sensitive axis. Moreover, the approximation error increases significantly when sensors are placed either too close to the coils or at an unfavorable angular coupling. To address this issue, we propose a sensor localization and orientation method that combines magnetic dipole-equivalent modeling with a rigid coil structure (RCS). The RCS provides stable geometric constraints and eliminates uncertainties introduced by scalp-attached coils. In addition, three objective functions (the standard Frobenius norm, a weighted Frobenius norm and the structural similarity index (SSIM)) are formulated to mitigate the imbalance caused by near-field strong signals and to improve stability under noise and error propagation. Simulation results demonstrate that both under ideal conditions and with assembly perturbations, the weighted Frobenius norm and SSIM methods consistently achieve position errors below 1 mm and orientation errors below 1°, which effectively suppress large outlier deviations and achieve better performance than the standard Frobenius norm. The results confirm the effectiveness of the proposed method in achieving both high accuracy and robustness. Beyond clarifying the primary factors influencing magnetic dipole approximation errors, this study provides a geometry-constrained and optimization-based framework, offering a feasible pathway toward the practical implementation of high-precision, multi-channel OPM-MEG systems.

Optically pumped magnetometers (OPMs) present a promising opportunity to advance magnetoencephalography (MEG), enhancing the accuracy of neuronal activity recordings due to their high spatiotemporal resolution. However, to fully realize the potential of OPM-MEG as an emerging brain functional imaging technology, it is essential to measure key indicators of neural dynamics, particularly phase–amplitude coupling (PAC). PAC is a fundamental mechanism for integrating information across different frequency bands and plays an important role in various cognitive functions and neurological disorders. Therefore, measuring PAC with OPM-MEG is a crucial step toward expanding its applications. In this study, brain signals under pitch sequence stimulation were recorded using OPM-MEG to analyze the PAC effect in the primary auditory cortex (Aud) and the inferior frontal gyrus (IFG), as well as the functional connectivity between brain regions. The findings were validated through EEG control experiments. The results indicated that the PAC effect measured by OPM-MEG was largely consistent with that measured by EEG, with OPM-MEG appearing to detect PAC more prominently under the current experimental conditions. The PAC of Aud exhibited a trend of initially increasing and then decreasing centered on the target pitch, showing hemispheric symmetry. The PAC of IFG showed variations under different pitch conditions and displayed right hemisphere lateralization. Functional connectivity analysis provided convergent evidence for the mechanisms underlying the PAC effect and suggested the reliability of the OPM-MEG system in capturing cross-frequency neural dynamics. To our knowledge, this study provides the first task-based evidence that OPM-MEG can measure PAC effects in cortical regions, offering an initial foundation for future investigations of brain dynamics using this technology.