The accuracy in electroencephalography (EEG) and combined EEG and magnetoencephalography (MEG) source reconstructions as well as in optimized transcranial electric stimulation (TES) depends on the conductive properties assigned to the head model, and most importantly on individual skull conductivity. In this study, we present an automatic pipeline to calibrate head models with respect to skull conductivity based on the reconstruction of the P20/N20 response using somatosensory evoked potentials and fields. In order to validate in a well-controlled setup without interplay with numerical errors, we evaluate the accuracy of this algorithm in a 4-layer spherical head model using realistic noise levels as well as dipole sources at different eccentricities with strengths and orientations related to somatosensory experiments. Our results show that the reference skull conductivity can be reliably reconstructed for sources resembling the generator of the P20/N20 response. In case of erroneous assumptions on scalp conductivity, the resulting skull conductivity parameter counterbalances this effect, so that EEG source reconstructions using the fitted skull conductivity parameter result in lower errors than when using the standard value. We propose an automatized procedure to calibrate head models which only relies on non-invasive modalities that are available in a standard MEG laboratory, measures under in vivo conditions and in the low frequency range of interest. Calibrated head modeling can improve EEG and combined EEG/MEG source analysis as well as optimized TES.

脑电图 (EEG) 和联合脑电图和脑磁图 (MEG) 源重建以及优化的经颅电刺激 (TES) 的准确性取决于分配给头部模型的导电特性，最重要的是取决于个体颅骨的电导率。在这项研究中，我们提出了一种自动管道，用于基于使用体感诱发电位和场重建 P20/N20 响应的颅骨电导率来校准头部模型。为了在不受数值误差影响的情况下在良好控制的设置中进行验证，我们使用真实的噪声水平以及具有与体感相关的强度和方向的不同偏心率的偶极子源来评估该算法在 4 层球形头部模型中的准确性实验。我们的结果表明，对于类似于 P20/N20 响应发生器的源，可以可靠地重建参考颅骨电导率。在头皮电导率假设错误的情况下，产生的颅骨电导率参数抵消了这种影响，因此使用拟合的颅骨电导率参数重建脑电图源导致的误差低于使用标准值时的误差。我们提出了一种自动化程序来校准头部模型，该程序仅依赖于标准 MEG 实验室中可用的非侵入性方式，测量下 因此，使用拟合的颅骨电导率参数重建脑电图源的误差低于使用标准值时的误差。我们提出了一种自动化程序来校准头部模型，该程序仅依赖于标准 MEG 实验室中可用的非侵入性方式，测量下 因此，使用拟合的颅骨电导率参数重建脑电图源的误差低于使用标准值时的误差。我们提出了一种自动化程序来校准头部模型，该程序仅依赖于标准 MEG 实验室中可用的非侵入性方式，测量下在体内条件和感兴趣的低频范围内。校准的头部建模可以改进 EEG 和 EEG/MEG 组合源分析以及优化的 TES。