In FEM-based EEG and MEG source analysis, the subtraction approach has been proposed to simulate sensor measurements generated by neural activity. While this approach possesses a rigorous foundation and produces accurate results, its major downside is that it is computationally prohibitively expensive in practical applications. To overcome this, we developed a new approach, called the localized subtraction approach. This approach is designed to preserve the mathematical foundation of the subtraction approach, while also leading to sparse right-hand sides in the FEM formulation, making it efficiently computable. We achieve this by introducing a cut-off into the subtraction, restricting its influence to the immediate neighborhood of the source. In this work, this approach will be presented, analyzed, and compared to other state-of-the-art FEM right-hand side approaches. Furthermore, we discuss how to arrive at an efficient and stable implementation. We perform validation in multi-layer sphere models where analytical solutions exist. There, we demonstrate that the localized subtraction approach is vastly more efficient than the subtraction approach. Moreover, we find that for the EEG forward problem, the localized subtraction approach is less dependent on the global structure of the FEM mesh when compared to the subtraction approach. Additionally, we show the localized subtraction approach to rival, and in many cases even surpass, the other investigated approaches in terms of accuracy. For the MEG forward problem, we show the localized subtraction approach and the subtraction approach to produce highly accurate approximations of the volume currents close to the source. The localized subtraction approach thus reduces the computational cost of the subtraction approach to an extent that makes it usable in practical applications without sacrificing rigorousness and accuracy.
翻译:在基于 FEM 的 EEG 和 MEG 源分析中,提出了模拟神经活动产生的传感器测量的减法,以模拟神经活动产生的传感器测量方法。虽然这一方法有一个严格的基础,并产生准确的结果,但其主要的下行是,在实际应用中,该方法的计算成本过高。要克服这一点,我们制定了一个新的方法,称为本地化减法。该方法旨在维护减法的数学基础,同时导致FEM 的配方的右侧,从而导致减法的减法稀少,使其能有效相容。我们通过在减法中引入减法,将其影响限制在源的近邻。在这项工作中,这一方法的主要下行是,该方法的精确度是严格的基点,分析,并与其他最先进的FEM 右侧方法相比,该方法的精确度如何实现高效和稳定的执行?我们通过多层次的边界模型进行验证,这证明本地化法比减法的减法效率要大得多。此外,我们发现,对于EG 近处的问题,本地化法的减法使得目前的精确程度更不依赖于全球的运用程度,因此,我们更低的递减法则显示更精确性方法的递减法。</s>