A Multivariate analysis on evoked components of Chinese semantic congruity: an OP-MEG study with EEG.

The application of wearable magnetoencephalography using optically-pumped magnetometers has drawn extensive attention in the field of neuroscience. Electroencephalogram system can cover the whole head and reflect the overall activity of a large number of neurons. The efficacy of optically-pumped magnetometer in detecting event-related components can be validated through electroencephalogram results. Multivariate pattern analysis is capable of tracking the evolution of neurocognitive processes over time. In this paper, we adopted a classical Chinese semantic congruity paradigm and separately collected electroencephalogram and optically-pumped magnetometer signals. Then, we verified the consistency of optically-pumped magnetometer and electroencephalogram in detecting N400 using mutual information index. Multivariate pattern analysis revealed the difference in decoding performance of these two modalities, which can be further validated by dynamic/stable coding analysis on the temporal generalization matrix. The results from searchlight analysis provided a neural basis for this dissimilarity at the magnetoencephalography source level and the electroencephalogram sensor level. This study opens a new avenue for investigating the brain's coding patterns using wearable magnetoencephalography and reveals the differences in sensitivity between the two modalities in reflecting neuron representation patterns.

Expanding the clinical application of OPM-MEG using an effective automatic suppression method for the dental brace metal artifact.

Optically pumped magnetometer magnetoencephalography (OPM-MEG) holds significant promise for clinical functional brain imaging due to its superior spatiotemporal resolution. However, effectively suppressing metallic artifacts, particularly from devices such as orthodontic braces and vagal nerve stimulators remains a major challenge, hindering the wider clinical application of wearable OPM-MEG devices. A comprehensive analysis of metal artifact characteristics from time, frequency, and time-frequency perspectives was conducted for the first time using an OPM-MEG device in clinical medicine. This study focused on patients with metal orthodontics, examining the modulation of metal artifacts by breath and head movement, the incomplete regular sub-Gaussian distribution, and the high absolute power ratio in the 0.5-8 Hz band. The existing metal artifact suppression algorithms applied to SQUID-MEG, such as fast independent component analysis (FastICA), information maximization (Infomax), and algorithms for multiple unknown signal extraction (AMUSE), exhibit limited efficacy. Consequently, this study introduced the second-order blind identification (SOBI) algorithm, which utilized multiple time delays for the component separation of OPM-MEG measurement signals. We modified the time delays of the SOBI method to improve its efficacy in separating artifact components, particularly those in the ultralow frequency range. This approach employs the frequency-domain absolute power ratio, root mean square (RMS) value, and mutual information methods to automate the artifact component screening process. The effectiveness of this method was validated through simulation experiments involving four subjects in both resting and evoked experiments. In addition, the proposed method was also validated by the actual OPM-MEG evoked experiments of three subjects. Comparative analyses were conducted against the FastICA, Infomax, and AMUSE algorithms. Evaluation metrics included normalized mean square error, normalized delta band power error, RMS error, and signal-to-noise ratio, demonstrating that the proposed method provides optimal suppression of metal artifacts. This advancement holds promise for enhancing data quality and expanding the clinical applications of OPM-MEG.

Source imaging method based on diagonal covariance bases and its applications to OPM-MEG.

Magnetoencephalography (MEG) is a noninvasive imaging technique used in neuroscience and clinical research. The source estimation of MEG involves solving a highly underdetermined inverse problem, which requires additional constraints to restrict the solution space. Traditional methods tend to obscure the extent of the sources. However, an accurate estimation of the source extent is important for studying brain activity or preoperatively estimating pathogenic regions. To improve the estimation accuracy of the extended source extent, the spatial constraint of sources is employed in the Bayesian framework. For example, the source is decomposed into a linear combination of validated spatial basis functions, which is proved to improve the source imaging accuracy. In this work, we further construct the spatial properties of the source using the diagonal covariance bases (DCB), which we summarize as the source imaging method SI-DCB. In this approach, specifically, the covariance matrix of the spatial coefficients is modeled as a weighted combination of diagonal covariance basis functions. The convex analysis is used to estimate noise and model parameters under the Bayesian framework. Extensive numerical simulations showed that SI-DCB outperformed five benchmark methods in accurately estimating the location and extent of patch sources. The effectiveness of SI-DCB was verified through somatosensory stimulation experiments performed on a 31-channel OPM-MEG system. The SI-DCB correctly identified the source area where each brain response occurred. The superior performance of SI-DCB suggests that it can provide a template approach for improving the accuracy of source extent estimations under a sparse Bayesian framework.

Study on cerebral oxygen saturation in children with sleep-disordered breathing.

To explore the association between the severity of sleep-disordered breathing, different types of respiratory events, peripheral oxygen saturation (SpO2), age and sleep stage on cerebral oxygen saturation (rSO2) in children. We enrolled children aged 4-14 years who were treated for snoring or mouth breathing at the Sleep Center of Beijing Children's Hospital, from February 2022 to July 2022. All children completed polysomnography, and SpO2, rSO2, and heart rate (HR) were recorded synchronously. A total of 70 children were included, including 16 (22.9%) with primary snoring, 38 (54.3%) with mild obstructive sleep apnea (OSA), and 16 (22.9%) with moderate-to-severe OSA. There were no significant differences in the mean rSO2 or minimum rSO2 among the primary snoring, mild OSA, and moderate-to-severe OSA groups (all p > 0.05). A total of 1119 respiratory events were included in the analysis. Regardless of the type of respiratory event, rSO2 and HR changes occur prior to fluctuations in SpO2. A mixed-effects model showed that ΔrSO2 was positively correlated with ΔSpO2, duration of respiratory event, mixed and obstructive apnea, central apnea, while negatively correlated with age and rapid eye movement (REM) sleep stage (all p < 0.05). Larger rSO2 fluctuations were impacted by a greater ΔSpO2, longer duration of respiratory events, younger age, apnea-related respiratory events and non-REM sleep stage. Thus, sleep disordered breathing in younger children warrants more attention. More research is needed to determine whether REM sleep has special protective effects on rSO2.

Global research trends of uranium-containing wastewater treatment based on bibliometric review.

The generation of uranium-containing wastewater (UCW) during different stages of uranium mining, processing, and utilization presents a significant ecological and biospheric threat. Consequently, it is crucial for both sustainable development and the protection of human health to adopt appropriate methods for the treatment of UCW as well as the separation and enrichment of uranium. This study conducted a comprehensive search of the Web of Science Core Collection (WOSCC) database for publications related to UCW treatment between 1990 and 2022 to gain insight into current trends in the field. Subsequently, the annual publications, WOSCC categories, geographical distribution, major collaborations, prolific authors, influential journals, and highly cited publications were the subjects of a biliometric analysis that was subsequently carried out. The study findings indicate a significant rise in the overall number of publications in the research field between 1990 and 2022. China, India, and the USA emerged as the primary contributors in terms of publication count. The Chinese Academy of Sciences, the East China University of Technology, and the University of South China were identified as the key research institutions in this field. Furthermore, a majority of the publications in this field were distributed through prestigious journals with high impact factors, such as the Journal of Radioanalytical and Nuclear Chemistry. The top 3 journals were Radioanalytical and Nuclear Chemistry, Chemical Engineering Journal, and Journal of Hazardous Materials. The keyword co-occurrence and burst analysis revealed that the current research on UCW treatment mainly focuses on adsorption-based treatment methods, environmentally functional materials, uranium recovery, etc. Furthermore, the study of the adsorption efficiency of different adsorbent materials, as well as the strengthening and improvement of adsorbent material selectivity and capacity for the recovery of uranium, represents a research hotspot in the field of UCW treatment in the future. This study conducts a comprehensive overview of the current status and prospects of the UCW treatment, which can provide a valuable reference for gaining insights into the development trajectory of the UCW treatment.

Detection of pediatric obstructive sleep apnea using a multilayer perceptron model based on single-channel oxygen saturation or clinical features.

PURPOSE: This study was performed to develop and evaluate a method of detecting pediatric obstructive sleep apnea (OSA) using a multilayer perceptron (MLP) model based on single-channel nocturnal oxygen saturation (SpO2) with or without clinical data. METHODS: Polysomnography data for 888 children with OSA and 417 unaffected children were included. An MLP model was proposed based on the features obtained from SpO2 and combined features of SpO2 and clinical data to screen symptomatic children for OSA. The performance of the overall classification was evaluated with the receiver operating characteristics curve and the metrics of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+), negative likelihood ratio (LR-), and accuracy. RESULTS: The sensitivity, specificity, PPV, NPV, LR+, LR-, and accuracy of the MLP model for SpO2 of an obstructive apnea-hypopnea index (OAHI) cutoff value of 1, 5, and 10 were 0.62-0.96, 0.11-0.97, 0.70-0.81, 0.55-0.93, 1.08-21.0, 0.39-0.39, and 0.69-0.91, respectively. The area under the receiver operating characteristics curve of an OAHI cutoff value of 1, 5, and 10 was 0.720, 0.842, and 0.922, respectively. After adding the clinical data of age, sex, body mass index, weight category, adenoid grade, or tonsil scale, the performance of the MLP model was basically at the same level as only single-channel SpO2. CONCLUSIONS: Application of this MLP model using single-channel SpO2 in children with snoring has high accuracy in the diagnosis of moderate to severe OSA but a poor effect in the diagnosis of mild OSA. The combination of clinical data did not significantly improve the diagnostic performance of the MLP model.

Optimization of Signal Space Separation for Optically Pumped Magnetometer in Magnetoencephalography.

Magnetoencephalography (MEG) is a noninvasive functional neuroimaging modality but highly susceptible to environmental interference. Signal space separation (SSS) is a method for improving the SNR to separate the MEG signals from external interference. The origin and truncation values of SSS significantly affect the SSS performance. The origin value fluctuates with respect to the helmet array, and determining the truncation values using the traversal method is time-consuming; thus, this method is inappropriate for optically pumped magnetometer (OPM) systems with flexible array designs. Herein, an automatic optimization method for the SSS parameters is proposed. Virtual sources are set inside and outside the brain to simulate the signals of interest and interference, respectively, via forward model, with the sensor array as prior information. The objective function is determined as the error between the signals from simulated sources inside the brain and the SSS reconstructed signals; thus, the optimized parameters are solved inversely by minimizing the objective function. To validate the proposed method, a simulation analysis and MEG auditory-evoked experiments were conducted. For an OPM sensor array, this method can precisely determine the optimized origin and truncation values of the SSS simultaneously, and the auditory-evoked component, for example, N100, can be accurately located in the temporal cortex. The proposed optimization procedure outperforms the traditional method with regard to the computation time and accuracy, simplifying the SSS process in signal preprocessing and enhancing the performance of SSS denoising.

Scanning multi-channel spin-exchange relaxation-free atomic magnetometer with high spatial and time resolution: publisher's note.

This publisher's note contains a correction to Opt. Lett.47, 3908 (2022)10.1364/OL.465832.

Scanning a multi-channel spin-exchange relaxation-free atomic magnetometer with high spatial and time resolution.

The emerging multi-channel spin-exchange relaxation-free (SERF) atomic magnetometer is a promising candidate for non-intrusive biomagnetism imaging. In this study, we propose a scanning 9-channel SERF magnetometer based on an acousto-optic modulator (AOM). Using the diffraction light of the AOM as the probe laser (with a low laser power of 1.7 mW), 9 channels were rapidly scanned by altering the diffraction angle. The scanning imaging scheme provides a new, to the best of our knowledge, approach for multi-channel magnetic field measurement and realizes a single-channel sensitivity of about 3 fT/Hz1/2, a spatial resolution of 0.6 mm, and a time resolution of about 2.7 ms, which is well suited for real-time extremely weak magnetic field imaging.

Medical Image Classification Based on Semi-Supervised Generative Adversarial Network and Pseudo-Labelling.

Deep learning has substantially improved the state-of-the-art in object detection and image classification. Deep learning usually requires large-scale labelled datasets to train the models; however, due to the restrictions in medical data sharing and accessibility and the expensive labelling cost, the application of deep learning in medical image classification has been dramatically hindered. In this study, we propose a novel method that leverages semi-supervised adversarial learning and pseudo-labelling to incorporate the unlabelled images in model learning. We validate the proposed method on two public databases, including ChestX-ray14 for lung disease classification and BreakHis for breast cancer histopathological image diagnosis. The results show that our method achieved highly effective performance with an accuracy of 93.15% while using only 30% of the labelled samples, which is comparable to the state-of-the-art accuracy for chest X-ray classification; it also outperformed the current methods in multi-class breast cancer histopathological image classification with a high accuracy of 96.87%.

Double-loop control and intelligent parameter tuning for the temperature control system of a DBR semiconductor laser.

Aiming at lower startup power consumption, stronger thermal load adaptability, easier parameters adjustment, and higher parameter tuning efficiency for the temperature control system of a distributed Bragg reflector (DBR) semiconductor laser, this paper employs the double-loop control and intelligent parameter tuning methods. First, the thermal equivalent circuit model is established for the laser temperature control system, which has stronger thermal load adaptability than the traditional transfer function model. In order to improve the modeling speed and accuracy, a mean impact value (MIV) quantum particle swarm optimization (QPSO) intelligent algorithm is proposed to tune the model parameters. A double-loop temperature control system is set up on this basis. Then, the MIV-QPSO intelligent algorithm is used to tune the control parameters, which shortens the settling time, increases the tuning efficiency, and improves the temperature control effect. The feasibility and effectiveness of the proposed methods are verified through the MATLAB/Simulink simulation of the laser temperature control process.

Automatic Estimation of the Interference Subspace Dimension Threshold in the Subspace Projection Algorithms of Magnetoencephalography Based on Evoked State Data.

A class of algorithms based on subspace projection is widely used in the denoising of magnetoencephalography (MEG) signals. Setting the dimension of the interference (external) subspace matrix of these algorithms is the key to balancing the denoising effect and the degree of signal distortion. However, most current methods for estimating the dimension threshold rely on experience, such as observing the signal waveforms and spectrum, which may render the results too subjective and lacking in quantitative accuracy. Therefore, this study proposes a method to automatically estimate a suitable threshold. Time-frequency transformations are performed on the evoked state data to obtain the neural signal of interest and the noise signal in a specific time-frequency band, which are then used to construct the objective function describing the degree of noise suppression and signal distortion. The optimal value of the threshold in the selected range is obtained using the weighted-sum method. Our method was tested on two classical subspace projection algorithms using simulation and two sensory stimulation experiments. The thresholds estimated by the proposed method enabled the algorithms to achieve the best waveform recovery and source location error. Therefore, the threshold selected in this method enables subspace projection algorithms to achieve the best balance between noise removal and neural signal preservation in subsequent MEG analyses.

Sampling clustering based on multi-view attribute structural relations.

In light of the exponential growth in information volume, the significance of graph data has intensified. Graph clustering plays a pivotal role in graph data processing by jointly modeling the graph structure and node attributes. Notably, the practical significance of multi-view graph clustering is heightened due to the presence of diverse relationships within real-world graph data. Nonetheless, prevailing graph clustering techniques, predominantly grounded in deep learning neural networks, face challenges in effectively handling multi-view graph data. These challenges include the incapability to concurrently explore the relationships between multiple view structures and node attributes, as well as difficulties in processing multi-view graph data with varying features. To tackle these issues, this research proposes a straightforward yet effective multi-view graph clustering approach known as SLMGC. This approach uses graph filtering to filter noise, reduces computational complexity by extracting samples based on node importance, enhances clustering representations through graph contrastive regularization, and achieves the final clustering outcomes using a self-training clustering algorithm. Notably, unlike neural network algorithms, this approach avoids the need for intricate parameter settings. Comprehensive experiments validate the supremacy of the SLMGC approach in multi-view graph clustering endeavors when contrasted with prevailing deep neural network techniques.

nnSegNeXt: A 3D Convolutional Network for Brain Tissue Segmentation Based on Quality Evaluation.

Accurate and automated segmentation of brain tissue images can significantly streamline clinical diagnosis and analysis. Manual delineation needs improvement due to its laborious and repetitive nature, while automated techniques encounter challenges stemming from disparities in magnetic resonance imaging (MRI) acquisition equipment and accurate labeling. Existing software packages, such as FSL and FreeSurfer, do not fully replace ground truth segmentation, highlighting the need for an efficient segmentation tool. To better capture the essence of cerebral tissue, we introduce nnSegNeXt, an innovative segmentation architecture built upon the foundations of quality assessment. This pioneering framework effectively addresses the challenges posed by missing and inaccurate annotations. To enhance the model's discriminative capacity, we integrate a 3D convolutional attention mechanism instead of conventional convolutional blocks, enabling simultaneous encoding of contextual information through the incorporation of multiscale convolutional features. Our methodology was evaluated on four multi-site T1-weighted MRI datasets from diverse sources, magnetic field strengths, scanning parameters, temporal instances, and neuropsychiatric conditions. Empirical evaluations on the HCP, SALD, and IXI datasets reveal that nnSegNeXt surpasses the esteemed nnUNet, achieving Dice coefficients of 0.992, 0.987, and 0.989, respectively, and demonstrating superior generalizability across four distinct projects with Dice coefficients ranging from 0.967 to 0.983. Additionally, extensive ablation studies have been implemented to corroborate the effectiveness of the proposed model. These findings represent a notable advancement in brain tissue analysis, suggesting that nnSegNeXt holds the promise to significantly refine clinical workflows.

Source Extent Estimation in OPM-MEG: A Two-Stage Champagne Approach.

The accurate estimation of source extent using magnetoencephalography (MEG) is important for the study of preoperative functional localization in epilepsy. Conventional source imaging techniques tend to produce diffuse or focused source estimates that fail to capture the source extent accurately. To address this issue, we propose a novel method called the two-stage Champagne approach (TS-Champagne). TS-Champagne divides source extent estimation into two stages. In the first stage, the Champagne algorithm with noise learning (Champagne-NL) is employed to obtain an initial source estimate. In the second stage, spatial basis functions are constructed from the initial source estimate. These spatial basis functions consist of potential activation source centers and their neighbors, and serve as spatial priors, which are incorporated into Champagne-NL to obtain a final source estimate. We evaluated the performance of TS-Champagne through numerical simulations. TS-Champagne achieved more robust performance under various conditions (i.e., varying source extent, number of sources, signal-to-noise level, and correlation coefficients between sources) than Champagne-NL and several benchmark methods. Furthermore, auditory and median nerve stimulation experiments were conducted using a 31-channel optically pumped magnetometer (OPM)-MEG system. The validation results indicated that the reconstructed source activity was spatially and temporally consistent with the neurophysiological results of previous OPM-MEG studies, further demonstrating the feasibility of TS-Champagne for practical applications.

A Novel Time-Frequency Parameterization Method for Oscillations in Specific Frequency Bands and Its Application on OPM-MEG.

Time-frequency parameterization for oscillations in specific frequency bands reflects the dynamic changes in the brain. It is related to cognitive behavior and diseases and has received significant attention in neuroscience. However, many studies do not consider the impact of the aperiodic noise and neural activity, including their time-varying fluctuations. Some studies are limited by the low resolution of the time-frequency spectrum and parameter-solved operation. Therefore, this paper proposes super-resolution time-frequency periodic parameterization of (transient) oscillation (STPPTO). STPPTO obtains a super-resolution time-frequency spectrum with Superlet transform. Then, the time-frequency representation of oscillations is obtained by removing the aperiodic component fitted in a time-resolved way. Finally, the definition of transient events is used to parameterize oscillations. The performance of this method is validated on simulated data and its reliability is demonstrated on magnetoencephalography. We show how it can be used to explore and analyze oscillatory activity under rhythmic stimulation.

Source Imaging Method based on Spatial Smoothing and Edge Sparsity (SISSES) and Its Application to OPM-MEG.

Source estimation in magnetoencephalography (MEG) involves solving a highly ill-posed problem without a unique solution. Accurate estimation of the time course and spatial extent of the source is important for studying the mechanisms of brain activity and preoperative functional localization. Traditional methods tend to yield small-amplitude diffuse or large-amplitude focused source estimates. Recently, the structured sparsity-based source imaging algorithm has emerged as one of the most promising algorithms for improving source extent estimation. However, it suffers from a notable amplitude bias. To improve the spatiotemporal resolution of reconstructed sources, we propose a novel method called the source imaging method based on spatial smoothing and edge sparsity (SISSES). In this method, the temporal dynamics of sources are modeled using a set of temporal basis functions, and the spatial characteristics of the source are represented by a first-order Markov random field (MRF) model. In particular, sparse constraints are imposed on the MRF model residuals in the original and variation domains. Numerical simulations were conducted to validate the SISSES. The results demonstrate that SISSES outperforms benchmark methods for estimating the time course, location, and extent of patch sources. Additionally, auditory and median nerve stimulation experiments were performed using a 31-channel optically pumped magnetometer MEG system, and the SISSES was applied to the source imaging of these data. The results demonstrate that SISSES correctly identified the source regions in which brain responses occurred at different times, demonstrating its feasibility for various practical applications.

Decoding N400m Evoked Component: A Tutorial on Multivariate Pattern Analysis for OP-MEG Data.

Multivariate pattern analysis (MVPA) has played an extensive role in interpreting brain activity, which has been applied in studies with modalities such as functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG) and Electroencephalography (EEG). The advent of wearable MEG systems based on optically pumped magnetometers (OPMs), i.e., OP-MEG, has broadened the application of bio-magnetism in the realm of neuroscience. Nonetheless, it also raises challenges in temporal decoding analysis due to the unique attributes of OP-MEG itself. The efficacy of decoding performance utilizing multimodal fusion, such as MEG-EEG, also remains to be elucidated. In this regard, we investigated the impact of several factors, such as processing methods, models and modalities, on the decoding outcomes of OP-MEG. Our findings indicate that the number of averaged trials, dimensionality reduction (DR) methods, and the number of cross-validation folds significantly affect the decoding performance of OP-MEG data. Additionally, decoding results vary across modalities and fusion strategy. In contrast, decoder type, resampling frequency, and sliding window length exert marginal effects. Furthermore, we introduced mutual information (MI) to investigate how information loss due to OP-MEG data processing affect decoding accuracy. Our study offers insights for linear decoding research using OP-MEG and expand its application in the fields of cognitive neuroscience.

Source localization comparison and combination of OPM-MEG and fMRI to detect sensorimotor cortex responses.

BACKGROUND AND OBJECTIVES: The exploration of various neuroimaging techniques have become focal points within the field of neuroscience research. Magnetoencephalography based on optically pumped magnetometers (OPM-MEG) has shown significant potential to be the next generation of functional neuroimaging with the advantages of high signal intensity and flexible sensor arrangement. In this study, we constructed a 31-channel OPM-MEG system and performed a preliminary comparison of the temporal and spatial relationship between magnetic responses measured by OPM-MEG and blood-oxygen-level-dependent signals detected by functional magnetic resonance imaging (fMRI) during a grasping task. METHODS: For OPM-MEG, the ß-band (15-30 Hz) oscillatory activities can be reliably detected across multiple subjects and multiple session runs. To effectively localize the inhibitory oscillatory activities, a source power-spectrum ratio-based imaging method was proposed. This approach was compared with conventional source imaging methods, such as minimum norm-type and beamformer methods, and was applied in OPM-MEG source analysis. Subsequently, the spatial and temporal responses at the source-level between OPM-MEG and fMRI were analyzed. RESULTS: The effectiveness of the proposed method was confirmed through simulations compared to benchmark methods. Our demonstration revealed an average spatial separation of 10.57 ± 4.41 mm between the localization results of OPM-MEG and fMRI across four subjects. Furthermore, the fMRI-constrained OPM-MEG localization results indicated a more focused imaging extent. CONCLUSIONS: Taken together, the performance exhibited by OPM-MEG positions it as a potential instrument for functional surgery assessment.

OPM-MEG Measuring Phase Synchronization on Source Time Series: Application in Rhythmic Median Nerve Stimulation.

The magnetoencephalogram (MEG) based on array optically pumped magnetometers (OPMs) has the potential of replacing conventional cryogenic superconducting quantum interference device. Phase synchronization is a common method for measuring brain oscillations and functional connectivity. Verifying the feasibility and fidelity of OPM-MEG in measuring phase synchronization will help its widespread application in the study of aforementioned neural mechanisms. The analysis method on source-level time series can weaken the influence of instantaneous field spread effect. In this paper, the OPM-MEG was used for measuring the evoked responses of 20Hz rhythmic and arrhythmic median nerve stimulation, and the inter-trial phase synchronization (ITPS) and inter-reginal phase synchronization (IRPS) of primary somatosensory cortex (SI) and secondary somatosensory cortex (SII) were analysed. The results find that under rhythmic condition, the evoked responses of SI and SII show continuous oscillations and the effect of resetting phase. The values of ITPS and IRPS significantly increase at the stimulation frequency of 20Hz and its harmonic of 40Hz, whereas the arrhythmic stimulation does not exhibit this phenomenon. Moreover, in the initial stage of stimulation, the ITPS and IRPS values are significantly higher at Mu rhythm in the rhythmic condition compared to arrhythmic. In conclusion, the results demonstrate the ability of OPM-MEG in measuring phase pattern and functional connectivity on source-level, and may also prove beneficial for the study on the mechanism of rhythmic stimulation therapy for rehabilitation.