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The optically pumped magnetometer (OPM) based magnetoencephalography (MEG) system offers advantages such as flexible layout and wearability. However, the position instability or jitter of OPM sensors can result in bad channels and segments, which significantly impede subsequent preprocessing and analysis. Most common methods directly reject or interpolate to repair these bad channels and segments. Direct rejection leads to data loss, and when the number of sensors is limited, interpolation using neighboring sensors can cause significant signal distortion and cannot repair bad segments present in all channels. Therefore, most existing methods are unsuitable for OPM-MEG systems with fewer channels. We introduce an automatic bad segments and bad channels repair method for OPM-MEG, called Repairbads. This method aims to repair all bad data and reduce signal distortion, especially capable of automatically repairing bad segments present in all channels simultaneously. Repairbads employs Riemannian Potato combined with joint decorrelation to project out artifact components, achieving automatic bad segment repair. Then, an adaptive algorithm is used to segment the signal into relatively stable noise data chunks, and the source-estimate-utilizing noise-discarding algorithm is applied to each chunk to achieve automatic bad channel repair. We compared the performance of Repairbads with the Autoreject method on both simulated and real auditory evoked data, using five evaluation metrics for quantitative assessment. The results demonstrate that Repairbads consistently outperforms across all five metrics. In both simulated and real OPM-MEG data, Repairbads shows better performance than current state-of-the-art methods, reliably repairing bad data with minimal distortion. The automation of this method significantly reduces the burden of manual inspection, promoting the automated processing and clinical application of OPM-MEG. [Display omitted] •Repairbads method automatically repairs bad channels and segments in OPM-MEG data.•Repairbads outperforms state-of-the-art methods, minimizing signal distortion.•This approach reduces manual intervention, promoting automated OPM-MEG processing.

Optically pumped magnetometer-based magnetoencephalography (OPM-MEG) is an novel non-invasive functional imaging technique that features more flexible sensor configurations and wearability; however, this also increases the requirement for environmental noise suppression. Subspace projection algorithms are widely used in MEG to suppress noise. However, in OPM-MEG systems with a limited number of channels, subspace projection methods that rely on spatial oversampling exhibit reduced performance. The homogeneous field correction (HFC) method resolves this problem by constructing a low-rank spatial model; however, it cannot address complex non-homogeneous noise. The spatiotemporal extended homogeneous field correction (teHFC) method uses multiple orthogonal projections to suppress disturbances. However, the signal and noise subspace are not completely orthogonal, limiting enhancement in the capabilities of the teHFC. Therefore, we propose an extended homogeneous field correction method based on oblique projection (opHFC), which overcomes the issue of non-orthogonality between the signal and noise subspace, enhancing the ability to suppress complex interferences. The opHFC constructs an oblique projection operator that divides the signals into internal and external components, eliminating complex interferences through temporal extension. We compared the opHFC with four benchmark methods by simulations and auditory and somatosensory evoked OPM-MEG experiments. The results demonstrate that opHFC provides superior noise suppression with minimal distortion, enhancing the signal quality at the sensor and source levels. Our method offers a novel approach to reducing interference in OPM-MEG systems, expanding their application scenarios, and providing high-quality signals for scientific research and clinical applications based on OPM-MEG. •The method addresses the issue of environmental interference suppression in OPM-MEG.•The algorithm extended homogeneous field correction method based on oblique projection.•The method effectively suppresses complex noise and outperforms existing methods.

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. [Display omitted] •The atomic magnetometers are getting more and more attention.•The metallic artifacts hinder the clinical application of wearable OPM-MEG.•The proposed method in this article can effectively suppress the dental metal artifact.

Major depressive disorder (MDD) is a prevalent mental illness characterized by persistent sadness, loss of interest in activities, and significant functional impairment. It poses severe risks to individuals’ physical and psychological well-being. The development of automated diagnostic systems for MDD is essential to improve diagnostic accuracy and efficiency. Electroencephalography (EEG) has been extensively utilized in MDD diagnostic research. However, studies employing deep learning methods still face several challenges, such as difficulty in extracting effective information from EEG signals and risks of data leakage due to experimental designs. These issues result in limited generalization capabilities when models are tested on unseen individuals, thereby restricting their practical application. In this study, we propose a novel deep learning approach, termed TSF-MDD, which integrates temporal, spatial, and frequency-domain information. TSF-MDD first applies a data reconstruction scheme to obtain a four-dimensional temporal–spatial–frequency representation of EEG signals. These data are then processed by a model based on 3D-CNN and CapsNet, enabling comprehensive feature extraction across domains. Finally, a subject-independent data partitioning strategy is employed during training and testing to eliminate data leakage. The proposed approach achieves an accuracy of 92.1%, precision of 90.0%, recall of 94.9%, and F1-score of 92.4%, respectively, on the Mumtaz2016 public dataset. The results demonstrate that TSF-MDD exhibits excellent generalization performance.

Magnetoencephalography (MEG) is a non-invasive imaging technique that captures neural activity with high spatio-temporal resolution. In recent years, novel wearable devices based on Optically Pumped Magnetometer (OPM) have emerged as a new driving force for advancing MEG due to their cost-effectiveness, portability, and mobility. In practical applications, MEG signals are frequently influenced by various interference sources, resulting in degradation of signal quality. Consequently, numerous suppression techniques have been proposed to overcome these challenges. This manuscript presents a comprehensive review of the most advanced methods for suppressing MEG noise or artifacts, with a specific focus on mitigating background noise, physiological artifacts (such as those caused by heartbeat, eye movements, and muscle contractions), as well as technical artifacts (including system-related artifacts associated with devices, motion-induced artifacts, and metal-induced artifacts). Additionally, the current limitations and challenges of these approaches in real-world scenarios are highlighted. Reviewing nearly a decade of research, there is an urgent need for a lightweight noise analysis framework in the complex measurement environment of wearable OPM-MEG devices. This framework should be capable of effectively detecting, classifying, and suppressing individual and combined MEG interference. By addressing this need, we can enhance the reliability and practicality of MEG signals while advancing brain science research. •Review of noise suppression methods in SQUID & OPM-MEG systems.•Analysis of interference solutions (background, physiological, non-physiological artifacts).•Challenges & future directions for wearable OPM-MEG, emphasizing lightweight frameworks.

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.

Background Public libraries in the United States have experienced increases in opioid-related substance use in their communities and on their premises. This includes fatal and non-fatal overdose events. Some libraries have adopted response measures in their branches to deter substance use or prevent overdose. A small number of libraries around the nation have decided to stock the opioid antagonist naloxone (Narcan) for staff to administer to patrons who experience overdose. This response measure has generated extensive media attention. Although Ohio ranks fourth in age-adjusted drug mortality rate in the United States, there has been no investigation of whether Ohio libraries are observing opioid-related transactions, consumption, and/or overdose events, or which measures they have adopted in response to these activities. We conducted a multimethod survey with Ohio public library directors to identify the response measures they have adopted. We present descriptive findings from the quantitative and qualitative items in our survey. Methods We conducted a cross-sectional 54-item multimethod survey of public library system directors (one per system) in Ohio. Directors of each of Ohio’s public library systems were invited to participate via email. Results Of 251 library systems, 56 responded (22.3% response rate), with 34 respondents (60.7%) indicating awareness of opioid-related transactions, consumption, and/or overdose on their premises. Most ( n  = 43, 76.8%) did not stock naloxone in their buildings. Over half ( n  = 34, 60.7%) reported implementing one or more non-naloxone response measures. These measures focus on improving security for staff and patrons, deterring opioid-related transactions (purchases and exchanges) and consumption, and providing educational events on substance use. Nearly half ( n  = 25, 47.2%) partner with community organizations to provide opioid response measures. A similar proportion reported adequate funding to respond to opioid-related substance use ( n  = 23, 45.1%), and most ( n  = 38, 74.5%) reported adequate support from their boards and communities. Few respondents have implemented evaluations of their response measures. Conclusions Ohio public libraries are responding to evidence of opioid-related transactions, consumption, and/or overdose on their premises with a range of measures that focus on substance use prevention and deterrence. Most Ohio library systems do not stock naloxone. Respondents indicated they prefer to call 911 and let first responders handle overdose events. The majority of respondents indicated their library systems have political capacity to respond to evidence of opioid-related substance use on their premises, but have limited operational and functional capacity. Findings suggest the need to revisit assumptions that public libraries are willing to stock naloxone to respond to overdose events, and that libraries have the resources to respond robustly to opioid-related transactions, consumption, and/or overdose on their premises.

In gas transmission, the regulator needs to adjust the gas pressure from high to low. The pressure energy can be then recovered by an expander, and the expander can drive a generator to produce electricity. However, the gas pressure regulator system and generator torque process often present difficult adjustment of PI parameters, and strong non-linearity of the hysteresis comparator and switching table in the traditional direct torque control (DTC) cause difficulties in the controller design and lead to large fluctuations of the generator torque. This paper designs a model predictive controller (MPC) for the gas pressure regulator process to reduce generator torque fluctuations. Simultaneously, a fuzzy PI controller is designed for the generator rotational speed process, and an MPC controller is exploited for the torque process; they operate in a cascaded manner. The fuzzy PI controller is used to calculate the torque set point. And the MPC controller is designed to obtain the optimal voltage vector of the generator for improving control performance through time delay compensation. The simulation experimental results highlight that the fluctuation of the regulator outlet gas pressure is reduced by 7.9% and 8.1%, and the output torque range is reduced by 3.4% and 2.1% compared with the traditional PI control and fuzzy PI control, respectively. The generator torque fluctuation range is reduced by 82.3%, the rotational speed fluctuation range is reduced by 76.9%, and the three-phase current fluctuation range is reduced by 76.6% compared with the traditional DTC.

BackgroundDue to economic pressure and environmental impact, college students often hesitate when planning their travels, resulting in anxiety to some extent. The conventional treatment method has relatively little effect on the treatment of college students’ travel anxiety disorder, so the study will use alprazolam combined with biofeedback therapy to intervene and treat college students’ travel anxiety disorder.Subjects and Methods72 college students with travel anxiety disorder from a certain university were selected as the research subjects and divided into Group C and Group D according to the driver allocation method. Group C received traditional medication or psychological intervention, while Group D received a combination of alprazolam and biofeedback therapy. After the experiment, the data was processed using SPSS 20.0 statistical software.ResultsThe number of effective cases in Group D reached 30, with an effective rate of 95.38%, while Group C only had 81.54%, significantly lower than Group D; After treatment, the anxiety score of Group D was 29.1 ± 3.6, lower than Group C’s 39.0 ± 3.8, and significantly lower than the 41.7 ± 3.9 before the experiment; After treatment, the number of nausea and vomiting in Group D was 1, appetite loss was 3, and hair loss was 2 points, both lower than those in Group C. The incidence of adverse reactions after treatment was relatively low.ConclusionsThe combination of alprazolam and biofeedback therapy is effective in the intervention and treatment of travel anxiety disorder in college students.