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.

It is known that a typical hyper-spectral image consists of a sequence of wavelength bands and each wavelength band illustrates a two-dimensional image. This paper presents a HSI classification method including two approaches. The first approach is a probabilistic algorithm for object classification based on the image illustrated by single hyper-spectral wavelength bands, respectively, which generate a sequence of registered object images. Since hyper-spectral images (HSIs) obtained by air-borne or satellite-borne remote sensors has limited size, which may affect the classification accuracy, especially for small objects due to lack of pixels. The second approach investigates the hyper-spectral characteristics for a specific object in terms of uniformed hyper-spectrum energy function in wavelength domain. The hyper-spectral characteristics are applied to object verification, especially for objects which are difficult to distinguish from each other using classic classification methods, so that the accuracy is improved. The classification accuracy for the proposed object-oriented characterization method is evaluated in terms of producer accuracy (PA) and Kappa coefficient based on hyper-spectral images obtained from air-borne or satellite-borne remote sensors, respectively. The numerical results demonstrate that the proposed method can effectively identify and verify objects in hyper-spectral image, especially for those objects that are difficult to be distinguished from each other by classic methods. Furthermore, the proposed object classification method based on hyper-spectral characteristics is compared to conventional methods, including spectral information divergence (SID) method and multiple spectral angle mapper-Markov random fields (MSAM-MRF) method. It can be seen that the accuracy of object classification achieved by the proposed method can be up to 20% higher than that of classic methods.

Optically pumped magnetometer OPM-MEG has the potential to replace the traditional low-temperature superconducting quantum interference device SQUID-MEG. Coherence analysis can be used to evaluate the functional connectivity and reflect the information transfer process between brain regions. In this paper, a finger tapping movement paradigm based on auditory cues was used to measure the functional signals of the brain using OPM-MEG, and the coherence between the primary motor cortex (M1) and the primary motor area (PM) was calculated and analyzed. The results demonstrated that the coherence of the three frequency bands of Alpha (8–13 Hz), Beta (13–30 Hz), and low Gamma (30–45 Hz) and the selected reference signal showed roughly the same position, the coherence strength and coherence range decreased from Alpha to low Gamma, and the coherence coefficient changed with time. It was inferred that the change in coherence indicated different neural patterns in the contralateral motor cortex, and these neural patterns also changed with time, thus reflecting the changes in the connection between different functional areas in the time-frequency domain. In summary, OPM-MEG has the ability to measure brain coherence during finger movements and can characterize connectivity between brain regions.

The high spatiotemporal resolution of optically pumped magnetometers (OPMs) makes them an essential tool for functional brain imaging, enabling accurate recordings of neuronal activity. However, physiological signals such as eye blinks and cardiac activity overlap with neural magnetic signals in the frequency domain, resulting in contamination and creating challenges for the observation of brain activity and the study of neurological disorders. To address this problem, an automatic physiological artifact removal method based on OPM magnetic reference signals and a channel attention mechanism is proposed. The randomized dependence coefficient (RDC) is employed to evaluate the correlation between independent components and reference signals, enabling reliable recognition of artifact components and the construction of training and testing datasets. A channel attention mechanism is subsequently introduced, which fuses features from global average pooling (GAP) and global max pooling (GMP) layers through convolution to establish a data-driven automatic recognition model. The backbone network is further optimized to enhance performance. Experimental results demonstrate a strong correlation between the magnetic reference signals and artifact components, confirming the reliability of magnetic signals as artifact references for OPM-MEG. The proposed model achieves an artifact recognition accuracy of 98.52% and a macro-average score of 98.15%. After artifact removal, both the event-related field (ERF) responses and the signal-to-noise ratio (SNR) are significantly improved. Leveraging the flexible and modular characteristics of OPM-MEG, this study introduces an artifact recognition framework that integrates magnetic reference signals with an attention mechanism. This approach enables highly accurate automatic recognition and removal of OPM-MEG artifacts, paving the way for real-time, automated data analysis in both scientific research and clinical applications.

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.

Optically pumped magnetometer magnetoencephalography (OPM-MEG) represents a novel method for recording neural signals in the brain, offering the potential to measure critical neuroimaging characteristics such as effective brain networks. Effective brain networks describe the causal relationships and information flow between brain regions. In constructing effective brain networks using Granger causality, the noise in the multivariate autoregressive model (MVAR) is typically assumed to follow a Gaussian distribution. However, in experimental measurements, the statistical characteristics of noise are difficult to ascertain. In this paper, a Granger causality method based on a cost-reference particle filter (CRPF) is proposed for constructing effective brain networks under unknown noise conditions. Simulation results show that the average estimation errors of the MVAR model coefficients using the CRPF method are reduced by 53.4% and 82.4% compared to the Kalman filter (KF) and maximum correntropy filter (MCF) under Gaussian noise, respectively. The CRPF method reduces the average estimation errors by 88.1% and 85.8% compared to the MCF under alpha-stable distribution noise and the KF method under pink noise conditions, respectively. In an experiment, the CRPF method recoversthe latent characteristics of effective connectivity of benchmark somatosensory stimulation data in rats, human finger movement, and auditory oddball paradigms measured using OPM-MEG, which is in excellent agreement with known physiology. The simulation and experimental results demonstrate the effectiveness of the proposed algorithm and OPM-MEG for measuring effective brain networks.