Here we propose that much of the magnetic interference observed when using optically pumped magnetometers for MEG experiments can be modeled as a spatially homogeneous magnetic field. We show that this approximation reduces sensor level variance and substantially improves statistical power. This model does not require knowledge of the underlying neuroanatomy nor the sensor positions. It only needs information about the sensor orientation. Due to the model's low rank there is little risk of removing substantial neural signal. However, we provide a framework to assess this risk for any sensor number, design or subject neuroanatomy. We find that the risk of unintentionally removing neural signal is reduced when multi-axis recordings are performed. We validated the method using a binaural auditory evoked response paradigm and demonstrated that removing the homogeneous magnetic field increases sensor level SNR by a factor of 3. Considering the model's simplicity and efficacy, we suggest that this homogeneous field correction can be a powerful preprocessing step for arrays of optically pumped magnetometers.

在这里，我们建议在使用光泵磁力计进行 MEG 实验时观察到的大部分磁干扰可以建模为空间均匀的磁场。我们表明，这种近似减少了传感器水平的变化，并大大提高了统计能力。该模型不需要了解潜在的神经解剖学或传感器位置。它只需要有关传感器方向的信息。由于模型的低等级，移除大量神经信号的风险很小。然而，我们提供了一个框架来评估任何传感器数量、设计或主题神经解剖学的这种风险。我们发现，在进行多轴记录时，无意中移除神经信号的风险会降低。