Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (MNE-Python, FieldTrip, DAiSS (SPM12), and Brainstorm) using datasets both with and without SSS interference suppression. We analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with the signal-to-noise ratio (SNR) in all four toolboxes. When applied carefully to MEG data with a typical SNR (3–15 dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs for the first three toolboxes while Brainstorm showed greater robustness but with lower spatial resolution. We also found that the SNR improvement offered by SSS led to more accurate localization.

中文翻译：

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MEG 源定位的波束形成器实现比较

波束成形器用于估计测量 MEG/EEG 信号下的神经元源的时空特性。几个 MEG 分析工具箱包括线性约束最小方差 (LCMV) 波束成形器的实现。然而，实现和结果的差异使波束形成器的选择和应用复杂化，并可能阻碍它们在研究和临床应用中的更广泛采用。此外，不同 MEG 传感器类型（例如磁力计和平面梯度计）的组合以及用于干扰抑制的预处理方法（例如信号空间分离 (SSS)）的应用，可能会以不同方式影响不同实现方式的结果。到目前为止，还没有对不同的实现进行系统的评估。这里，我们使用具有和不具有 SSS 干扰抑制的数据集在四个广泛使用的开源工具箱（MNE-Python、FieldTrip、DAiSS (SPM12) 和 Brainstorm）中比较了 LCMV 波束成形器管道的定位性能。我们分析了 i) 模拟的 MEG 数据，ii) 从静态和移动的幻影记录，iii) 从接受听觉、视觉和体感刺激的健康志愿者记录。我们还研究了 SSS 的影响以及磁力计和梯度计信号的组合。我们在所有四个工具箱中量化了定位误差和点传播量如何随信噪比 (SNR) 变化。当小心地应用于具有典型 SNR (3–15 dB) 的 MEG 数据时，所有四个工具箱都能可靠地定位源；然而，它们对预处理参数的敏感性不同。正如预期的那样，定位在非常低的 SNR 下非常不可靠，但我们发现前三个工具箱在非常高的 SNR 下也存在高定位误差，而 Brainstorm 表现出更强的鲁棒性，但空间分辨率较低。我们还发现 SSS 提供的 SNR 改进导致更准确的定位。