Magnetoencephalography (MEG), leveraging magnetic signal detection, is a clinically validated neuroimaging technique known for its high spatiotemporal resolution and non-invasive nature, effectively mitigating damage to brain function and mortality rates in preoperative epilepsy evaluations. Optically pumped magnetometer (OPM), a quantum sensor, emerges as the new generation of MEG detection device benefiting from its low cost, wearable, motion robustness, and lifespan-compliant. While in the context of medical applications, the OPM-MEG system requires overcoming several barriers. Magnetic field interference has the propensity to impact the OPM, triggering output errors and thus diminish the quality of neuromagnetic imaging. In this review, we thoroughly explore the advantages and limitations of hardware-based magnetic field interference suppression techniques, and envision the implementable suppression strategies focusing on the pressing demand for the mobile MEG. The anticipated iterative enhancements in magnetic field interference suppression technology hold the promise of driving the application of OPM-MEG in more naturalistic, multi-person interactive paradigms in cognitive neuroscience, thus facilitating the development of novel therapeutic approaches for neurological and psychiatric disorders.

Aiming at the issue of substantial nonlinear error arising from triaxial DC magnetic field interferences in wearable and mobile measurement scenarios, a triaxial magnetic fields response model of single-beam atomic SERF magnetometer under non-zero magnetic condition is established, revealing that the effect of triaxial DC magnetic field interferences on the detected AC responses exhibits the nonlinear feature, while their impact on the DC response manifests as the interval-linear characteristic. Consequently, a closed-loop control system for mixed AC and DC magnetic fields is proposed. By leveraging the DC response within the feedback signal, a localized linearization control strategy is implemented across the triaxial magnetic fields, enabling direct and real-time suppression of DC interferences, and subsequently reducing their nonlinear impact on the measured AC response. Among them, the nonlinear impact caused by x-axis DC magnetic field interference decreases from 48.12% in the open-loop configuration to 0.01%. Additionally, it takes less time to suppress the interferences to below 2%, shortened from 4.7 s to 32 ms.

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.

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.

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.