IEEE Trans Biomed Eng. 2022 Oct;69(10):3131-3141. doi: 10.1109/TBME.2022.3161830. Epub 2022 Sep 19.
Magnetoencephalography (MEG) is a non-invasive technique that measures the magnetic fields of brain activity. In particular, a new type of optically pumped magnetometer (OPM)-based wearable MEG system has been developed in recent years. Source localization in MEG can provide signals and locations of brain activity. However, conventional source localization methods face the difficulty of accurately estimating multiple sources. The present study presented a new parametric method to estimate the number of sources and localize multiple sources. In addition, we applied the proposed method to a constructed wearable OPM-MEG system.
We used spatial clustering of the dipole spatial distribution to detect sources. The spatial distribution of dipoles was obtained by segmenting the MEG data temporally into slices and then estimating the parameters of the dipoles on each data slice using the particle swarm optimization algorithm. Spatial clustering was performed using the spatial-temporal density-based spatial clustering of applications with a noise algorithm. The performance of our approach for detecting multiple sources was compared with that of four typical benchmark algorithms using the OPM-MEG sensor configuration.
The simulation results showed that the proposed method had the best performance for detecting multiple sources. Moreover, the effectiveness of the method was verified by a multimodel sensory stimuli experiment on a real constructed 31-channel OPM-MEG.
Our study provides an effective method for the detection of multiple sources.
With the improvement of the source localization methods, MEG may have a wider range of applications in neuroscience and clinical research.
脑磁图（MEG）是一种测量大脑活动磁场的非侵入性技术。近年来，一种新型基于光泵磁力计（OPM）的可穿戴 MEG 系统已经开发出来。MEG 中的源定位可以提供大脑活动的信号和位置。然而，传统的源定位方法面临着准确估计多个源的困难。本研究提出了一种新的参数方法来估计源的数量并定位多个源。此外，我们将提出的方法应用于构建的可穿戴 OPM-MEG 系统。
我们使用偶极子空间分布的空间聚类来检测源。偶极子的空间分布是通过将 MEG 数据在时间上分割成切片，然后使用粒子群优化算法在每个数据切片上估计偶极子的参数来获得的。使用基于时空密度的应用程序的空间-时间聚类噪声算法进行空间聚类。使用 OPM-MEG 传感器配置，比较了我们的方法与四种典型基准算法检测多个源的性能。
模拟结果表明，该方法在检测多个源方面具有最佳性能。此外，通过对真实构建的 31 通道 OPM-MEG 进行多模型感官刺激实验，验证了该方法的有效性。
本研究为多源检测提供了一种有效的方法。
随着源定位方法的改进，MEG 在神经科学和临床研究中可能有更广泛的应用。
