IEEE Trans Biomed Eng. 2021 Jul;68(7):2211-2221. doi: 10.1109/TBME.2020.3040373. Epub 2021 Jun 17.
Magnetoencephalography (MEG) signals typically reflect a mixture of neuromagnetic fields, subject-related artifacts, external interference and sensor noise. Even inside a magnetically shielded room, external interference can be significantly stronger than brain signals. Methods such as signal-space projection (SSP) and signal-space separation (SSS) have been developed to suppress this residual interference, but their performance might not be sufficient in cases of strong interference or when the sources of interference change over time.
Here we suggest a new method, extended signal-space separation (eSSS), which combines a physical model of the magnetic fields (as in SSS) with a statistical description of the interference (as in SSP). We demonstrate the performance of this method via simulations and experimental MEG data.
The eSSS method clearly outperforms SSS and SSP in interference suppression regardless of the extent of a priori information available on the interference sources. We also show that the method does not cause location or amplitude bias in dipole modeling.
Our eSSS method provides better data quality than SSP or SSS and can be readily combined with other SSS-based methods, such as spatiotemporal SSS or head movement compensation. Thus, eSSS extends and complements the interference suppression techniques currently available for MEG.
Due to its ability to suppress external interference to the level of sensor noise, eSSS can facilitate single-trial data analysis, exemplified in automated analysis of epileptic data. Such an enhanced suppression is especially important in environments with large interference fields.
脑磁图（MEG）信号通常反映了混合的神经磁场、与主体相关的伪迹、外部干扰和传感器噪声。即使在磁屏蔽室内，外部干扰也可能比脑信号强得多。已经开发了信号空间投影（SSP）和信号空间分离（SSS）等方法来抑制这种残余干扰，但在干扰较强或干扰源随时间变化的情况下，它们的性能可能不够。
在这里，我们提出了一种新的方法，扩展的信号空间分离（eSSS），它将磁场的物理模型（如 SSS 中）与干扰的统计描述（如 SSP 中）结合起来。我们通过模拟和实验 MEG 数据来演示这种方法的性能。
无论对干扰源的先验信息的程度如何，eSSS 方法在干扰抑制方面明显优于 SSS 和 SSP。我们还表明，该方法不会在偶极子建模中导致位置或幅度偏差。
我们的 eSSS 方法提供了比 SSP 或 SSS 更好的数据质量，并且可以很容易地与其他基于 SSS 的方法结合使用，如时空 SSS 或头部运动补偿。因此，eSSS 扩展并补充了目前可用于 MEG 的干扰抑制技术。
由于其能够将外部干扰抑制到传感器噪声水平，eSSS 可以促进单次试验数据分析，例如在癫痫数据的自动分析中。这种增强的抑制在干扰场较大的环境中尤为重要。
