论文标题
借助贝叶斯不确定性建模，来自脑电图记录的同时颅骨电导率和焦点源成像
Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the help of Bayesian Uncertainty Modelling
论文作者
论文摘要
脑电图（EEG）源成像问题对正在检查患者头骨的电气建模非常敏感。不幸的是，当前可用的脑电图设备及其嵌入式软件未考虑到这一点。相反，通常使用基于文献的颅骨电导率参数。在本文中，我们提出了一种基于贝叶斯近似误差方法的统计方法，以补偿由于未知的头骨电导率而导致的源成像误差，并同时计算了实际颅骨电导率值的低阶估计值。通过使用与焦点源活性相对应的模拟脑电图数据，我们证明了该方法重建未知颅骨电导率引起的潜在焦点源和低阶误差的潜力。随后，估计的误差用于近似颅骨电导率。结果表明，源定位准确性和可行的头骨电导率估计值明显改善。
The electroencephalography (EEG) source imaging problem is very sensitive to the electrical modelling of the skull of the patient under examination. Unfortunately, the currently available EEG devices and their embedded software do not take this into account; instead, it is common to use a literature-based skull conductivity parameter. In this paper, we propose a statistical method based on the Bayesian approximation error approach to compensate for source imaging errors due to the unknown skull conductivity and, simultaneously, to compute a low-order estimate for the actual skull conductivity value. By using simulated EEG data that corresponds to focal source activity, we demonstrate the potential of the method to reconstruct the underlying focal sources and low-order errors induced by the unknown skull conductivity. Subsequently, the estimated errors are used to approximate the skull conductivity. The results indicate clear improvements in the source localization accuracy and feasible skull conductivity estimates.
