Human brain activity is based on electrochemical processes, which can only be measured invasively. Therefore, quantities such as magnetic flux density (MEG) or electric potential differences (EEG) are measured non-invasively in medicine and research. The reconstruction of the neuronal current from the measurements is a severely ill-posed problem though its visualization is one of the main research tools in cognitive neuroscience. Here, using an isotropic multiple-shell model for the geometry of the head and a quasi-static approach for modeling the electro-magnetic processes, we derive a novel vector-valued spline method based on reproducing kernel Hilbert spaces. The presented vector spline method follows the path of former spline approaches and provides classical minimum norm properties. In addition, it minimizes the (infinite-dimensional) Tikhonov-Philips functional handling the instability of the inverse problem. This optimization problem reduces to solving a finite-dimensional system of linear equations without loss of information. It results in a unique solution which takes into account that only the harmonic and solenoidal component of the current affects the measurements. Besides, we prove a convergence result: the solution achieved by the vector spline method converges to the generator of the data as the number of measurements increases. The vector splines are applied to the inversion of synthetic test cases, where the irregularly distributed data situation could be handled very well. Combined with parameter choice methods, numerical results are shown with and without additional Gaussian white noise. Former approaches based on scalar splines are outperformed by the vector splines results with respect to the normalized root mean square error. Finally, reasonable results with respect to physiological expectations for real data are shown.
翻译:人类大脑活动以电化学过程为基础,只能进行侵入性测量。 因此, 磁通密度( MEG) 或电源潜在差异( EEG) 等数量在医学和研究中进行非侵入性测量。 从测量中重建神经内流是一个严重的问题, 尽管其视觉化是认知神经科学的主要研究工具之一。 这里, 使用一个用于头部几何的异位多壳模型和模拟电磁进程的一种准静态方法, 我们根据再生内核Hilbert空间, 得出一种新的矢量估值样板条方法。 显示的矢量螺丝样方法遵循前螺旋尊重方法的路径, 并提供典型的最小规范属性特性。 此外, 它尽量减少了( 无限度) Tikhonov- Philips 功能, 处理反向问题不稳定性。 这个优化问题可以降低到解决一个不丢失信息的线性方程式的有限维度系统。 它导致一个独特的解决方案, 仅考虑到当前内流结构空间空间空间的正向白值部分。 显示的矢量调整和单向方向值的螺旋值的螺旋值值,, 将数据向数据向数据向向数据向下显示一个结果, 。 我们通过数据向内流流流流流流流流流流结果显示的正正趋化结果, 显示一个对正正正值的正正正向向向向数据向数据向向向向向向向向值向值向值的正值向值向值向值向值向值向值向值向值向值向值向值向值向值向显示的正值的向值的向值的向值向值向值向值向值向值向值向值向值向显示的向值的向值的向值向值向值向值向值向值向值向值向值向值的向值向值向值向值向值向值向值向值向值结果,,, 。 。 。 。, 显示数据向值向值向值向向向向值向值向值向显示数据向值向值向值向值向值向值向值向值向值向值向值向值向值向值向值向值向值向值向向值向