Applications such as brain computer interfaces require recordings of relevant neuronal population activity with high precision, for example, with electrocorticography (ECoG) grids. In order to achieve this, both the placement of the electrode grid on the cortex and the electrode properties, such as the electrode size and material, need to be optimized. For this purpose, it is essential to have a reliable tool that is able to simulate the extracellular potential, i.e., to solve the so-called ECoG forward problem, and to incorporate the properties of the electrodes explicitly in the model. In this study, this need is addressed by introducing the first open-source pipeline, FEMfuns (finite element method for useful neuroscience simulations), that allows neuroscientists to solve the forward problem in a variety of different geometrical domains, including different types of source models and electrode properties, such as resistive and capacitive materials. FEMfuns is based on the finite element method (FEM) implemented in FEniCS and includes the geometry tessellation, several electrode-electrolyte implementations and adaptive refinement options. The Python code of the pipeline is available under the GNU General Public License version 3 at https://github.com/meronvermaas/FEMfuns. We tested our pipeline with several geometries and source configurations such as a dipolar source in a multi-layer sphere model and a five-compartment realistically-shaped head model. Furthermore, we describe the main scripts in the pipeline, illustrating its flexible and versatile use. Provided with a sufficiently fine tessellation, the numerical solution of the forward problem approximates the analytical solution. Furthermore, we show dispersive material and interface effects in line with previous literature. Our results indicate substantial capacitive and dispersive effects due to the electrode-electrolyte interface when using stimulating electrodes. The results demonstrate that the pipeline presented in this paper is an accurate and flexible tool to simulate signals generated on electrode grids by the spatiotemporal electrical activity patterns produced by sources and thereby allows the user to optimize grids for brain computer interfaces including exploration of alternative electrode materials/properties.

中文翻译：

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FEMfuns：体积传导建模管道，包括电阻、电容或色散组织和电极。

脑机接口等应用需要高精度记录相关神经元群活动，例如使用皮层电图 (ECoG) 网格。为了实现这一目标，需要优化电极网格在皮层上的放置以及电极属性（例如电极尺寸和材料）。为此，必须有一个能够模拟细胞外电位的可靠工具，即解决所谓的 ECoG 正向问题，并将电极的特性明确地纳入模型中。在本研究中，通过引入第一个开源管道 FEMfuns（有用神经科学模拟的有限元方法）来解决这一需求，它允许神经科学家解决各种不同几何领域（包括不同类型的源模型）中的正向问题和电极特性，例如电阻和电容材料。FEMfuns 基于 FEniCS 中实现的有限元方法 (FEM)，包括几何细分、多种电极电解质实现和自适应细化选项。管道的 Python 代码可根据 GNU 通用公共许可证版本 3 获取，网址为 https://github.com/meronvermaas/FEMfuns。我们使用多种几何形状和源配置测试了我们的管道，例如多层球体模型中的偶极源和五室逼真形状的头部模型。此外，我们描述了管道中的主要脚本，说明了其灵活和通用的用途。提供足够精细的细分，前向问题的数值解接近解析解。此外，我们展示了与先前文献一致的色散材料和界面效应。我们的结果表明，当使用刺激电极时，电极-电解质界面会产生显着的电容和色散效应。结果表明，本文提出的管道是一种准确而灵活的工具，可以模拟由源产生的时空电活动模式在电极网格上生成的信号，从而允许用户优化脑机接口的网格，包括探索替代电极材料/特性。