The MINI-magnetically shielded room (MINI-MSR) offers a near-zero magnetic environment for the high performance of optically pumped atomic magnetometers (OPMs) in magnetoencephalography (MEG) tests. It has the advantages, such as being small-sized, portable, lightweight, offering efficient space utilization, and being cost-effective. However, owing to its small size and narrow internal space, the MINI-MSR can only provide a limited uniform region with larger gradients and irregular magnetic noise distributions. Moreover, it is affected by large external background fluctuations. Once the external interference exceeds the magnetic shielding capacity of the MINI-MSR, the precision of OPMs is compromised. Therefore, an active magnetic compensation system (AMCS) composed of biplanar coils (BCs) is introduced to enlarge the uniform region, maintaining the inhomogeneity errors below 1% to meet the measurement accuracy. However, conventional algorithms for designing BCs struggle with searching for the regularization coefficient ω that determines the coil’s shape. The process lacks ergodicity and efficiency, posing challenges in obtaining maximum volume and uniformity within the target region. In this study, a new hybrid method (NHM) composed of the target field method (TFM), image method (IM), and novel artificial fish swarm algorithm (novel ASFA) is proposed to design BCs for the AMCS. The aim is to suppress background field fluctuations, enhancing the signal-to-noise ratio while addressing coupled errors to reduce larger gradients and magnetic noise levels. Experimental results show that the maximum inhomogeneity error of BCs is less than 1.23%, which is consistent with simulation results. The sensitivity of the AMCS at 0.01 Hz ranges from 180 to 0.16 pT/Hz ^1/2 , realizing the noise suppression ratio of 30.51 dB. Based on the closed-loop AMCS, the signal-to-noise ratio in the alpha rhythm generation task is effectively improved.

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.

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.

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.

Magnetocardiography (MCG) plays a growing role in non-invasive cardiac disease diagnosis. However, MCG signals are prone to environmental magnetic fields and metal artifacts, distorting waveforms and affecting diagnostic accuracy. Existing methods like the Fast Independent Component Analysis (FastICA) and Information Maximization (Infomax) algorithm have limitations in suppressing ultra-low frequency metal artifacts. We propose a Second-Order Blind Identification algorithm based on an optimized time-delay matrix, utilizing temporal coherence to effectively separate ultra-low frequency metal artifacts from mixed sources. An automatic screening method for metal artifacts, QRS, T/P waves, and unknown interferences is established using time-frequency features. Extensive simulations and real OPM-MCG experiments validate our method’s superiority in metal artifact suppression. The results show our method surpasses FastICA and Infomax in suppressing metal artifacts, achieving average SNR improvement of 5.36% to 29.40% across four subjects. Reconstructed P/QRS/T waves are undistorted, with a minimum 80.71% reduction in RMSE. This method potentially expands MCG’s clinical applications, benefiting more patients.

Deep brain stimulation (DBS) is a critical neuromodulation technique that has been widely applied in the treatment of neurological disorders such as Parkinson's disease (PD) and epilepsy. As an important functional neuroimaging modality, magnetoencephalography (MEG) has played a key role in DBS research. In particular, the next-generation MEG based on optically pumped magnetometers (OPM-MEG), offers greater potential for clinical applications. However, the strong electromagnetic interference generated by DBS systems makes data acquisition and analysis challenging in OPM-MEG recordings from patients with implanted devices. To the best of our knowledge, there have been no studies that systematically investigate the characteristics or suppression of DBS-induced artifacts in OPM-MEG recordings from human subjects. In this paper, we describe a novel OPM-MEG interference suppression algorithm called extended homogeneous field correction based on oblique projection (opHFC), developed for suppressing environmental noise in OPM-MEG. To illustrate the practical application of opHFC in clinical settings, particularly for patients with DBS implants. We evaluate the performance of opHFC in denoising OPM-MEG data from PD patients with DBS implants. By applying opHFC to real-world clinical data, we assess its ability to reduce DBS-induced artifacts while preserving neural activity patterns and conduct a comprehensive comparison between opHFC and several commonly used artifact suppression techniques in OPM-MEG. Our results show that opHFC significantly enhances signal quality and achieves the most effective suppression performance, demonstrating its potential as a reliable tool for advancing OPM-MEG applications in challenging clinical environments. This study highlights the practical value of opHFC in improving OPM-MEG data quality for PD patients with DBS, paving the way for more accurate neuroscientific research and clinical diagnostics.