Zeffiro接口（ZI），Sampsa-Pursiainen是一个开源代码包，包括一个基于EEG/MEG正向和反向模拟的可访问的有限元工具，还可用于其他针对大脑的生物电磁成像应用。
Zeffiro Interface (ZI), Sampsa Pursiainen © 2018, is an open source code package constituting an accessible tool for finite element (FE) based forward and inverse simulations in EEG/MEG and can be used also in other bioelectromagnetical imaging applications targeting the brain.
通过使用ZI，如果三角ASCII曲面网格（采用DAT或ASC文件格式）可用，则可以分割真实的多层几何体并生成多分割有限元网格。
With ZI, one can segment a realistic multilayer geometry and generate a multi-compartment FE mesh, if triangular ASCII surface grids (in DAT or ASC file format) are available.
例如，可以使用FreeSurfer软件套件生成合适的曲面分割（版权所有FreeSurfer，2013）。
A suitable surface segmentation can be produced, for example, with the FreeSurfer software suite (Copyright © FreeSurfer, 2013).
ZI还允许导入使用FreeSurfer创建的一个分组，以区分不同的大脑区域，从而分析时间序列上大脑功能的连接性。
ZI allows also importing a parcellation created with FreeSurfer to enable distinguishing different brain regions and, thereby, analysing
the connectivity of the brain function over a time series.
不同的腔室可以定义为活动的，允许分析皮层下的结构。
Different compartments can be defined as active, allowing the analysis of the sub-cortical strucures.
在每个分区中，活动的方向可以是正常约束的，也可以是无约束的。
In each compartment, the orientation of the activity can be either normally constrained or unconstrained.
在配备图形计算单元（GPU）的计算机中，ZI的主程序可以显著地加速。
The main routines of ZI can be accelerated significantly in a computer equipped with a graphics computing unit (GPU).
特别推荐使用GPU执行正向模拟过程，即生成FE网格、前导场矩阵和在不同点集之间进行插值。
It is especially recommendable to perform the forward simulation process, i.e., to generate the FE mesh, the lead field matrix and to interpolate between different point sets, utilizing a GPU.
在前向模拟阶段之后，模型也可以在没有GPU加速的情况下进行处理。
After the forward simulation phase, the model can be processed also without GPU acceleration.
接口基本功能的简要介绍可在以下网址找到：
A brief introduction to the essential features of the interface can be found at:
https://github.com/sampsapursiainen/zeffiro_interface/wiki
参考文献：
The interface itself has been introduced in:
He, Q., Rezaei, A. & Pursiainen, S. (2019). Zeffiro User Interface for Electromagnetic Brain Imaging: a GPU Accelerated FEM Tool for Forward and Inverse Computations in Matlab. Neuroinformatics, doi:10.1007/s12021-019-09436-9
The essential mathematical techniques used in the interface have been reviewed and validated in:
Miinalainen, T., Rezaei, A., Us, D., Nüßing, A., Engwer, C., Wolters, C. H., & Pursiainen, S. (2019). A realistic, accurate and fast source modeling approach for the EEG forward problem. NeuroImage, 184, 56-67.
Pursiainen, S. (2012). Raviart–Thomas-type sources adapted to applied EEG and MEG: implementation and results. Inverse Problems, 28(6), 065013.
The IAS MAP (iterative alternating sequential maximum a posteriori) inversion method is based on:
Calvetti, D., Hakula, H., Pursiainen, S., & Somersalo, E. (2009). Conditionally Gaussian hypermodels for cerebral source localization. SIAM Journal on Imaging Sciences, 2(3), 879-909.
It has been applied for a realistic brain geometry, e.g., in:
Lucka, F., Pursiainen, S., Burger, M., & Wolters, C. H. (2012). Hierarchical Bayesian inference for the EEG inverse problem using realistic FE head models: depth localization and source separation for focal primary currents. Neuroimage, 61(4), 1364-1382.
The current preserving source model combines linear (face-intersecting) and quadratic (edgewise) elements via the Position Based Optimization (PBO)method and the 10-source stencil in which 4 face sources and 6 edge sources are applied for each tetrahedral element containing a source:
Bauer, M., Pursiainen, S., Vorwerk, J., Köstler, H., & Wolters, C. H. (2015). Comparison study for Whitney (Raviart–Thomas)-type source models in finite-element-method-based EEG forward modeling. IEEE Transactions on Biomedical Engineering, 62(11), 2648-2656.
Pursiainen, S., Vorwerk, J., & Wolters, C. H. (2016). Electroencephalography (EEG) forward modeling via H (div) finite element sources with focal interpolation. Physics in Medicine & Biology, 61(24), 8502.
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