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.

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.

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.

The advent of optically pumped magnetometers (OPMs) facilitates the development of on-scalp magnetoencephalography (MEG). In particular, the triaxial OPM emerged recently, making simultaneous measurements of all three orthogonal components of vector fields possible. The detection of triaxial magnetic fields improves the interference suppression capability and achieves higher source localization accuracy using fewer sensors. The source localization accuracy of MEG is based on the accurate co-registration of MEG and MRI. In this study, we proposed a triaxial co-registration method according to combined principal component analysis and iterative closest point algorithms for use of a flexible cap. A reference phantom with known sensor positions and orientations was designed and constructed to evaluate the accuracy of the proposed method. Experiments showed that the average co-registered position errors of all sensors were approximately 1 mm and average orientation errors were less than 2.5° in the X -and Y orientations and less than 1.6° in the Z orientation. Furthermore, we assessed the influence of co-registration errors on the source localization using simulations. The average source localization error of approximately 1 mm reflects the effectiveness of the co-registration method. The proposed co-registration method facilitates future applications of triaxial sensors on flexible caps.