Zeffiro Interface (ZI), Sampsa Pursiainen © 2018是一个开源代码包，它构成了一个可访问的工具，用于在EEG/MEG中进行基于有限元（FE）的正向和反向模拟，也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. 

基于ZI软件，如果三角形的ASCII表面网格（采用DAT或ASC文件格式）可用，可以分割真实的多层几何体并生成多间隔的有限元网格。

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（Copyright © FreeSurfer, 2013）生成合适的曲面分段。

A suitable surface segmentation can be produced, for example, with the FreeSurfer software suite (Copyright © FreeSurfer, 2013). 

这样的分段可以作为一组ASC文件一次导入。

Such a segmentation can be imported at once as a set of ASC files. 

ZI还允许导入用FreeSurfer创建的分割图，以便区分不同的大脑区域，从而分析一个时间序列中大脑功能的连通性。

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. 

不同的间隔可以定义为活动的，允许分析皮质下的结构。

Different compartments can be defined as active, allowing the analysis of the sub-cortical strucures. 

在每个间隔中，活动的方向可以是正常约束的，也可以是不受约束的。

In each compartment, the orientation of the activity can be either normally constrained or unconstrained. 

在具备图形计算单元（GPU）的计算机中，ZI的主要程序可以得到显著加速。

The main routines of ZI can be accelerated significantly in a computer equipped with a graphics computing unit (GPU). 

特别建议使用GPU执行正向模拟过程，即生成有限元网格、导联场矩阵，并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. 

在前向仿真阶段之后，模型处理可以不采用GPU加速。

After the forward simulation phase, the model can be processed also without GPU acceleration.

ZI采用的主要数学技术来源于以下文献：

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.

IAS MAP（迭代交替序列极大值后验）逆方法基于以下文献：

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.

ZI被应用于以下实际的脑几何分析中：

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.

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.

完整源码下载地址：

http://page2.dfpan.com/fs/4lcj3221f291b656635/ 

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