EEGNET：开源的可视化分析脑网络工具

文章连接

名称：EEGNET: An Open Source Tool for Analyzing and Visualizing M/EEG Connectome

写在前面的话2

确实不大好用，出bug比较频繁，我会上传一些其他代码的安装包，不过最好还是自己改，吃透会好一些，看需要吧。

写在前面的话

翻译水平有限，所以附上原文可以及时对照。我已经把不太确定和可能有问题的标注了，如有错误请多多批评指正，感谢♪(･ω･)ﾉ

翻译这东西吧，不是说看不懂，但这么长的文章真的看着太痛苦了，而且个人翻译还要打字，更慢。事实证明真的机器翻译大行其道不是没有原因的。

安装

将安装包解压，添加到路径之下即可

数据

This file represents either the scalp EEG data or the reconstructed sources. The default file format is the ‘.mat’.

可以使用溯源或者直接的头皮脑电数据。数据格式为‘mat’

It should be a 3 dimensional matrix (Nc x Ns x Nt) where Nc, Ns and Nt are the channels (brain regions in the case of sources file) number, the sample size and the number of trials (Nt is considered 1 for data averaged over trials)

矩阵必须是三维的：(Nc × Ns × Nt) Nc是采集通道，Ns是一个trail的长度（也可能是被试人员，待测试），Nt是被试次数，对于在trail上做了平均的，Nt=1

respectively. When solving the inverse problem and for visualizing the network at scalp level, the electrode location file is required. In the current EEGNET version, both the.xyx and mat formats are supported.

注意，当求解溯源时和可视化头皮信号网络，需要电极位置的信息。在最新版本，xyz坐标文件（EEGlab打好标记可以生产）和mat文件（？）都可以。

Some basic preprocessing features are available in the current version of EEGNET. The imported data can be firstly visualized. These signals can be then filtered using a Finite Impulse Response (FIR) linear filter before computing the function connectivity (FC) matrices.

EEGNET可以做一些基础的预处理工作。加载的信号可以被显示出来.这些数据可以先进行FIR滤波，再计算功能连接（FC）矩阵。

This feature allows users to choose the frequency band where to compute the FC. The data can also resampled by changing the sampling frequency. This feature can be essential in some cases where users attempt to reduce the size of the data.

我们可以选择频带去计算FC，数据也可以改变采样频率重采样。在某些我们想要去减少数据大小的时候，这种方法就很重要

The preprocessing tool allows user also to specify the baseline from the visualized data. This baseline is essential for computing/normalizing the post vs. pre stimulus connectivity for instance or to compute the noise covariance matrix when solving the inverse problem.

这些预处理工具允许我们校正基线。对于像是计算和归一化刺激前连通性矩阵或者在解决求逆问题时计算噪声干扰相关矩阵，校正基线就显得十分重要。

EEGNET provides also the possibility of computing the time-frequency representation of the data. In the current version, the complex Morlet wavelet is used as it was shown to provide a good compromise between time and frequency resolution [40–42]. This time frequency maps can be shown trial by trial in the case of multi-trial data.

EEGNET也提供计算时频表示数据的功能。当前版本，支持复morlet小波变换，其作为时频解决方案具有良好的效果。在有多个trail的情况下，时频图将挨个显示

连接矩阵（The adjacency matrix）

This file is an Nc × Nc dimension. It contains the values of the functional connections between all the channels (or brain regions).

这个文件是Nc×Nc维度的矩阵。他包含着所有通道（脑区）之间的功能连接的值。

This file can be also in Ns × Nc × Nc in the case where it is the dynamics of functional networks that is being analyzed.

该文件也可以是Ns×Nc×Nc，如果需要进行功能网络的动态分析。

To compute the functional connectivity (FC) matrices, four methods are available: the cross-correlation, the mean phase coherence (MPC), the mutual information (MI) and the Phase Locking Value (PLV), see [43] for review.

计算功能连接矩阵有四种方法：互相关，平均相位相关，互信息，锁相值，以及其他方法[43]

After choosing the desired method, the connectivity values can be computed over scalp signals (generating 2D networks) or over the time series associated with the reconstructed sources (generating 3D networks at cortex level).

注意，头皮脑电只能计算2D，溯源可以计算3D

In the typical example presented in this paper, the Phase Locking Value (PLV) was computed between scalp electrodes as well as between sources. The PLV is a part of the method from PS family.

在论文中使用的是锁相（PLV）的方法，在头皮信号和溯源信号中都计算了一遍。PLV是PS方法的一种（一共有四种，幅值相位两两互锁）。

It was initially proposed by Lachaux et al. [44] and its main advantage is the possibility of computing FC matrix at each instant as the method look at the inter-trial information [2].

这种方法最开始提出于Lachaux 等人的文章中，并且他最主要的优点就是能够计算每个时刻的FCM，因为这种方法可以关注到 inter-trail（？）的信息

To assess the significance of the obtained connections, surrogates data analysis can be used and a level of significance can be set which allow users to keep only the statistically significant connections (see [2] for details about this approach).

为了获取得到的连接矩阵中的意义/内涵，应当使用代替数据分析法并设置一个有效阈值来只保留那些我们认为具有统计学显著意义的连接。（更多细节见参考文献2）

To ensure the significance of the obtained FC matrices, we integrated a statistical test based on the surrogate data analysis. Briefly, we use multivariate Fourier transform surrogates generated from the original EEG data. Such surrogates correspond to realizations of linear stationary process with conserved auto-and cross-correlation characteristics.

为了保证FCM的显著性，我们加入了基于代替数据法的统计学检验。简单来说，我们在原始EEG数据上使用了多元傅里叶变换替代法。这些函数对应于具有保守自相关和互相关特性的线性平稳过程的实现。

The null hypothesis is tested by comparing the original connectivity value (Corg) and those obtained using the surrogate data (Csurr) using a statistical test. The “Rank test” is used to reject or accept the null hypothesis.

通过比较原始连通性值(COLG)和使用统计检验的代理数据(Csurr)获得的连通性值，来验证零假设。“等级检验”用于拒绝或接受零假设。

Basically, [Corg; Csurr] is sorted in increasing order and the rank index for Corg is returned. With a number of surrogates (nsurr = 100 for example), if this rank is > 95 and < 5 (significance level at 95%), this means that it lies in the tail of the distribution, and that the null hypothesis can be rejected (two-tailed test) with a significance of p = 2*(1/ (nsurr+1)) = 0.019. The output of this analysis is the matrix containing only the significant connections.

基本上，[Corg；Csurr]按递增顺序排序，并返回COG的rank index。举个例子，有100个surrogates，如果rank 大于95或者小于5（显著性等级为95），就意味着他处于分布的尾部，并且是可以拒绝的（双尾检验）零假设（p = 0.019）。这样就得到了一个具有统计意义的连接矩阵。

网络（The Network）

When realizing M/EEG source connectivity, the cortical surface file and its corresponding scout file are required. The surface file contains the cortical mesh and the scout file contains the labels of the brain regions in case of using specific atlas. The cortical parcellation provides the ROIs that are used as network nodes in EEGNET.

当使用溯源连接时，需要皮质表面文件和相应的scout文件。皮质表面文件含有皮质网络，scout文件包含当使用特殊altas时需要的脑区标签。皮层区域分割提供作为EEGNET中网络节点的ROIS。

The surface files store geometric information about the morphology of the cortex. It can be created using the open source imaging analysis tool FreeSurfer http://surfer.nmr.mgh.harvard.edu/. The surface file can be also checked using brainstorm http://neuroimage.usc.edu/brainstorm/.

表面文件存储关于皮层形态的几何信息。它可以使用开源图像分析工具freesurfer创建。也可以用brainstorm检查。
The identification of the ROIs in the mat
rices is determined by a scout file which is a.mat file (can be generated also using Brainstorm). This file contains the labels of all the ROIs based on the already used atlas for segmentation such as Desikan [45] and Destrieux [46].

矩阵中ROIs的识别是由一个scout文件确定的，它是一个.mat文件(也可以使用Brainstorm生成)。该文件包含所有ROIs的标签，ROIs基于已经使用的用于分割的地图集，如Desikan[45]和Destrieux[46]生成。

常见图特征

A． 全局特征

• Density: the density of a graph is the fraction of present edges to all possible connections. If the density of a graph is 1 then it is a complete graph (every vertex is connected to every other vertex) 密度
• The characteristic path length: is the average shortest path lengths in the network 特征路径长度
• The global efficiency is the average inverse shortest path length in the network 全局效率
• Radius: The radius of a graph is the minimum graph eccentricity of any graph vertex in a graph. A disconnected graph therefore has infinite radius. Eccentricity is the maximum graph distance between a vertex v and any other vertex u of the graph. 半径
• Diameter: graph’s diameter is the largest number of vertices which must be traversed in order to travel from one vertex to another. 直径

B.Node parameters

• Degree: Node degree is the number of links connected to the node. In case of directed graph, the indegree is the number of inward links and the outdegree is the number of outward links, and the total degree is the sum of both indegree and outdegree. 度
• Clustering Coefficient: The clustering coefficient is the fraction of triangles around a node i.e. the fraction of node’s neighbors that are neighbors of each other. 聚类系数
• K-Coreness Coefficient: The k-core is the largest subgraph comprising nodes of degree at least k. The coreness of a node is k if the node belongs to the k-core but not to the (k+1)-core. K-系数
• Node Betweenness: Node betweenness centrality is the fraction of all shortest paths in the network that contain a given node. Nodes with high values of betweenness centrality participate in a large number of shortest paths. 节点介数
• Participation Coefficient: Compares the number of links (degree) of node i to nodes in all clusters with its number of links within its own cluster. The participation coefficient plays an important role in classifying the nodes of a graph as connector hubs and provincial hubs. 参与系数

C．Edge parameters

• Edge Betweenness: Edge betweenness centrality is the fraction of all shortest paths in the network that contain a given edge. Edges with high values of betweenness centrality participate in a large number of shortest paths. 边介数
• Shortcut Edges: Shortcuts are central edges which significantly reduce the characteristic path length in the network. 最短路径
• Edges Neighborhood Overlap: is the number of nodes that are neighbors of the nodes of that edge. 共有邻居

可视化

头皮水平（Scalp level.）

When the user is only interested in scalp networks, the EEG data should be firstly loaded. The functional connectivity is then computed among scalp signals, according to a pairwise procedure.

如果使用者只关心头皮信号构建的网络，可以先加载EEG数据。功能连接随后会依据内置的程序计算。

For visualizing the network and computing the network measures, the channel file is required. Users can also directly import their connectivity matrices (computed elsewhere) to compute the network measures and visualizing the network.

为了可视化网络和计算网络指标，需要通道文件。用户也可以直接导入连接矩阵来计算和可视化网络。

The channels position is a file with four columns, the first for the node number or label, the next three for x , y and z positions (an example of channel location file is contained within in the Examples folder included in the downloaded EEGNET).

通道位置文件有四个维度。第一个是通道节点的名称，后者是节点的xyz坐标。（在随着EEGNET下载的Examples中有位置文件的案例）

This part supports the static and dynamic option. The static option requires a 2D matrix (Nc × Nc) while the dynamic behavior option requires 3D matrices(Ns × Nc × Nc). Typical examples of the static and dynamic scalp networks are presented in Fig 2A and 2B respectively.

分析支持静态和动态网络。静态需要2D矩阵(Nc × Nc)，而3D矩阵需要(Ns × Nc × Nc). 典型案例如下图所示。

Fig.2 头皮网络
A)The different steps performed to obtain a ‘static’ scalp network. 不同步数下的静态网络
B) typical example of the dynamics of scalp networks obtained during a picture naming task (see [47] for about the data). The node color represents the modules and the size represents the strength values. 典型任务态动态头皮网络。节点颜色表示模块，大小表示强度（连接强度还是本身相关量高低待定）

皮质水平

To compute the brain networks at source level from M/EEG data, the inverse problem must be solved. It consists of reconstructing the brain sources from the scalp M/EEG.

为了计算溯源后的脑网络，求逆问题必须解决。溯源数据由来自头皮的M/EEG构成重建。

When the M/EEG signals are checked and approved for further analysis, the time series of the reconstructed sources can be estimated. After loading the coordinates of the electrodes as well as a brain surface mesh, the lead field matrix can be computed using different tools such as ‘OpenMEEG’ [48]. The time courses of the sources are then estimated by solving the inverse problem. Several algorithms for solving the inverse problem can be used (see [49] for review). In the example showed here, the weighted Minimum Norm Estimate (wMNE) was used [50].

当对M/EEG信号进行检验和进一步分析时，就可以估计溯源的时间序列。在加载电极坐标和脑表面网格后，可以使用不同的工具(如OpenMEEG[48])计算the lead field matrix。然后通过求解逆问题来估计源的时间序列。求解逆问题的几种算法可以使用([49])。在随后的例子中，使用了加权最小范数估计(WMNE)[50]。

This step can be performed within EEGNET or elsewhere (in Brainstorm for instance).
此步骤（？）可以在EEGNET或其他地方执行(例如，在Brainstorm中)。

The file containing the time series of the sources can be directly loaded as input for the next step, which consists in computing the functional connectivity, see [3] for comparison of several inverse algorithms and connectivity measures.

包含源的时间序列的文件可以作为下一步的输入直接加载，下一步包括计算函数的连通性，参见[3]几种反向算法和连通性度量的比较。

In addition, the user can directly import the connectivity matrix computed elsewhere (see Examples folder for an example of source level matrix). Once the connectivity matrices are obtained and loaded, a set of measures can be extracted from these matrices.

此外，用户可以直接导入其他地方计算的连接矩阵(有关源级矩阵的Example，请参见Examples文件夹)。一旦获得并加载了连通矩阵，就可以从这些矩阵中提取一组测量特征。

We integrated a number of network measures developed in the BCT toolbox [31]. EEGNET also provides the possibility of interacting with the different calculated network measures such as controlling the size and color of node (Fig 3) and edges (Fig 4).

我们集成了一些网络特征方法在BCT工具箱中。EEGNET也提供了使用其他不同计算网络测量特征方法的可能，比如使用控制节点和边沿的尺寸和颜色。

Figs 3 and 4 are typical examples representing the network obtained during picture naming task at 190ms-320ms segmented using k-means clustering tool of the functional connectivity [47].

图3和图4是一些典型案例，这些案例显示了使用k-means聚类方法功能连接工具，在190ms-320ms的片段，图片命名任务上的脑网络的样子。

First, Fig 3A shows the visualization of nodes on the cortex—without showing the edges—(modules are color-coded). As depicted, the obtained network contains two main modules. The thresholded network indicates the presence of two main modules. The first ones contain a nodes located in the bilateral occipital region and the second module is mainly located in the left frontal lobe. Second, Fig 3B shows the left view of the same network. It shows also the possibility for the user to show the corresponding atlas.

首先，图3A显示了皮层上节点的可视化–没有显示边–(模块是彩色编码的)。如图所示，阈值网络表示两个主要模块的存在。第一节位于双侧枕区，第二节主要位于左侧额叶。第二，图3B显示相同网络的左视图。同时也向用户展示了相应区域的可能。

Fig 4 displays the same network as the one showed in Fig 3 but with the addition of the edges. The edges can be also coded in color and size. In Fig 4A, the color represents the weight. Three options are available for coding the edges: i) show all edges with the same color, ii) using a specific color-map or iii) coding the edges in three different colors normalized to the highest weight values.

图4所展示的与图3中所示的网络相同，但是加上了连接（边）。边也可以用颜色和大小编码。在图4A中，颜色代表权重。有三个功能选项可用于编码边：一)显示所有颜色相同的边；二)使用特定的颜色绘制或 三)用三种不同颜色为边进行编码，这些颜色归一化为最高的权重值。（三？？）

Fig 4B shows the ‘multiview’ option that consists in showing the network from different views in the same figure. Different ‘multiview’ options are available. The user has also the option to customize his ‘multiview’ by selecting the desired views from the control panel.

图4b是“多视图”选项，他是在同一图（figure）中展示了不同视角下的网络的。不同的“多视图”选项是可用的。用户还可以通过从控制面板中选择所需的视图来自定义他的“多视图”。

量化指标（Quantification）

An essential feature of EEGNET is the possibility of quantifying the obtained networks. The network measures computed from the BCT toolbox can be visualized in a quantitative way as shown in Fig 5. The figure shows the results of the strength values of the different ROIs in the left and right hemispheres. It shows that the main ROIs involved in the network are the occipital regions such as occipital pole in the left hemisphere and the inferior occipital in the right hemisphere. Other features can be also chosen such as the efficiency, the degree, the clustering coefficient or any other desired measure. It requires only the selection of the measure in the control panel showed in Fig 5.

EEGNET的一个重要功能是对所获得的网络进行量化的可能。如图5所示，从BCT工具箱中计算出的网络测量特征可以定量地显示出来。图中显示了左右半球不同ROIs的强度值的结果。结果表明，该网络的主要参与区为左侧半球的枕骨极和右半球的下枕叶。还可以选择效率、程度、聚类系数或任何其他期望的度量等特征。它只需要在控制面板中选择如图5所示的测量方法即可。

实验结果（Experimental # results）

In this section, we show the difference steps realized using EEGNET to identify networks involved during picture naming task for a given subject. Participant was asked to name 148 displayed pictures on a screen. The brain activity was recorded using dense-EEG, 256 electrodes, system (EGI, Electrical Geodesic Inc.). EEG signals were collected with a 1 kHz sampling frequency. After loading the signal, to obtain the scalp level network, the functional connectivity was computed using PLV method at gamma band (30–45 Hz), Fig 6A. The signal shown in Fig 6A corresponds to the average signal over trials. The vertical blue line represents the onset time instant (presentation of the visual stimulus). In our case, 200ms were taken as pre-stimulus period. After computing the network measure, the node’s color and size represent the modularity and the degree respectively. The Fig 6B shows a mainly the occipital electrodes are involved in the period between 120–200ms。

在这一部分中，我们展示了在不同步骤上，使用EEGNET识别网络，该网络的任务是特定主题的图片的命名。参与者被要求在屏幕上列出148幅显示的图片。脑活动记录使用密集脑电图，256个电极，系统为EGI（Electrical Geodesic Inc出品)。脑电信号采集频率为1 kHz。加载信号后，在伽玛波段(30~45 Hz)用PLV法计算功能连接度，以获得头皮脑电网络，如图6A所示。图6A所示的信号对应于试验期间的平均信号。垂直蓝线表示起始时间瞬间(视觉刺激的呈现)。在我们的例子中，200毫秒作为刺激前的准备时间（period）。计算网络测度后，节点的颜色和大小分别表示模块属性和程度。图6b显示的主要是在120-200 ms时段之间的枕骨电极。

The source level network at the same period is shown in Fig 6C. The quantification of this network by computing the degree for each node shows that highest values correspond to the left/right inferior occipital, right occipital anterior and occipital pole (Fig 6D). These regions are well known to play a capital role in the processing of visual information and object recognition [51, 52]. Moreover, the gamma activity in this time period was shown to marker of object recognition and binding [52, 53]. The network in another period (190–320ms) was also illustrated and the corresponding degree values (Fig 6E and 6F). The network involves the left inferior temporal gyrus in addition to the inferior temporal sulcus. These regions were stated to be in direct relation to semantic processing (Martin & Chao, 2001). It is also the time window in which the N200 classically appear. The N200 is a marker of semantic processing in go/no-go tasks (Thorpe et al., 1996). For more details about the picture naming task and the networks corresponds to different periods, see [3].

如图6C所示的是在同一时间段的溯源网络。通过计算每个节点的度数对该网络进行量化显示，最高值对应于左/右枕下，右枕前和枕极（图6D）。众所周知，这些区域在视觉信息处理和物体识别中起着至关重要的作用[51，52]。此外，该时间段内的伽马活性被证明是物体识别和结合的标志[52，53]。还说明了另一个时间段（190-320ms）中的网络以及相应的度值（图6E和6F）。除了颞下沟，该网络还涉及左颞下回。据说这些区域与语义处理有直接关系（Martin＆Chao，2001）。这也是N200经典出现的时间窗口。 N200是执行/不执行任务中语义处理的标记（Thorpe等，1996）。有关图片命名任务和网络对应于不同时段的更多详细信息，请参见[3]。

技术细节（Technical # details）

EEGNET was developed using MATLAB as programming language with a user-friendly GUI under 64 bit Windows 7 environment, this toolbox has been successfully tested on different operating systems with MATLAB installed, including Windows 7, Linux and Mac OS under 64-bit versions. To facilitate the first use of EEGNET, tutorial and user manual documents are available in the download webpage that also provides the user with some examples for scalp and source networks. It is worth mentioning that EEGNET depends on other software tools. Some of these tools are written in MATLAB such as the BCT toolbox [31]. Preferably, the cortical surfaces and the scout files may be generated using Freesurfer [54] and checked/visualized using Brainstorm [30].

EEGNET是以MATLAB为编程语言，在64位Windows 7环境下，使用一个用户友好的图形用户界面，在安装了MATLAB的不同操作系统上，包括Windows 7、Linux和Mac操作系统，在64位版本下成功地进行了测试。为便于第一次使用EEGNET，教程和用户手册文件可在下载网页上查阅，该网页还为用户提供了一些头皮和源网络的示例。值得一提的是，EEGNET依赖于其他软件工具。其中一些工具是用MATLAB编写的，如BCT工具箱[31]。更进一步地，皮质表面和scout文件可以使用Freesurfer[54]生成，并使用Brainstorm[30]检查/可视化。

软件许可（Software # License）

EEGNET is licensed under the GNU General Public License version 1. This is a free software license, such that EEGNET may be freely redistributed and modified by any party. However, when distributing the software, the imposition of any restrictions on any further redistribution is forbidden

数据（Data）

The sample data used in the paper was approved by the National Ethics Committee for the Protection of Persons (CPP), conneXion study, agreement number (2012-A01227-36), promoter: Rennes University Hospital, Rennes, France. Participants provided their written informed consent to participate in the study.

讨论（discussion）

To identify networks from M/EEG data, at least four tools are required from loading/preprocessing the EEG data, solving the inverse problem, computing the functional connectivity, computing the network measures to then visualizing the identified networks in interactive way. However, researchers always look for reducing the number of tools they use to accomplish a complete data processing. As a network identification/visualization tool, EEGNET achieves most of these functions.

为了从M/EEG数据中识别网络，至少需要四个工具来加载/预处理EEG数据，解决反问题，计算功能连接性，计算网络度量，然后以交互方式可视化所识别的网络。然而，研究人员总是希望减少他们用来完成一个完整的数据处理的工具的数量。EEGNET作为一种网络识别/可视化工具，实现了其中的大部分功能。

In addition, the interactive analysis/visualization is a crucial part of scientific research. The easy visualization of data can inspire novel hypotheses, help researchers to quickly evaluate their results, and allow for significant quality control. EEGNET has novel interactive visualization features not available in existing software packages for visualization of the connectome such as the ability to interactively threshold networks based on the network measures for instance.

此外，交互分析/可视化是科学研究的重要组成部分。数据的可视化可以激发新的假设，帮助研究人员快速评估其结果，并允许其进行有效的质量把控。EEGNET在现有的连接体可视化软件包中没有提供新的交互可视化特性，例如基于网络度量的交互式阈值网络的能力。

数据收集方法（Approaches # for # data # collecting）

EEGNET provides all the steps from loading the EEG signals to the identification of the brain networks. However different files are needed to accomplish these steps. Here we show some suggestions to how obtain these files.

EEGNET提供了从加载EEG信号到识别大脑网络的所有步骤。但是，需要不同的文件来完成这些步骤。在这里，我们展示了如何获得这些文件的一些建议。

A.皮质层生成（The cortical surface.）

This file is very crucial to the analysis/visualization of the cortex level networks. Using the structural MRI of the participant (or template), FreeSurfer [54] can be used to compute all the different cortical parcellation with the correspondent different atlases. Performing all the cortical reconstruction steps, including subcortical segmentation for both hemispheres may take about ~16h for each MRI. The Destrieux and Desikan reconstructions atlases [45, 46] can be generated automatically using FreeSurfer and divide the cortical surface into parcels based on macroscopic sulcal and gyral profiles. The parcellation can be further subdivided into finer regions in order to generate for instance ~1000 regions (see [3, 10]). This parcellation can be realized/visualized in Brainstorm [30] and related.mat file could be exported. After choosing the desired spatial resolution (number of ROIs), the scout file contains the position and the label of each of the ROIs can be also exported. This exported file can be then used in EEGNET.

该文件对于皮层级网络的分析/可视化至关重要。通过使用参与者（或模板）的结构MRI，FreeSurfer [54]可用于计算具有相应的不同atlases（区域）的所有不同的皮质区域模块(cortical parcellation)。对于每个MRI，执行所有皮质重建步骤（包括两个半球的皮质下分割）可能需要约16小时。可以使用FreeSurfer自动生成Destrieux和Desikan重建图集[45、46]，然后根据宏观的龈沟和回旋轮廓将皮质表面分为多个区域。可以将分割进一步细分为更精细的区域，以便生成例如〜1000个区域（请参阅[3，10]）。这种分类可以在Brainstorm [30]中实现/可视化，并且可以导出related.mat文件。选择所需的空间分辨率（ROI的数量）后，scout文件将包含位置，并且每个ROIs的标签也可以导出。然后可以在EEGNET中使用此导出的文件。

B. 功能连接矩阵工具与指南（The functional connectivity matrices.）

The functional connectome is characterized by statistical independences between neural activities in different regions. In the M/EEG context, the FC is usually computed between signals recorded at the scalp signals using different methods such as cross-correlation [55, 56], phase locking value [44], nonlinear correlation coefficient [57, 58], phase lag index [59], imaginary coherence [60], mutual information [61] and others (see [43] for review). This can be realized in resting states or evoked activities. MNE python [62], Brainstorm [30], Brainwave (http://home.kpn.nl/stam7883/brainwave.html) and the MATLAB toolbox for FC [63] are open-source software packages with the ability to calculate many FC metrics from M/EEG data.

功能连接体的特征基于不同区域神经活动之间的统计独立性。 在M / EEG的文章中，通常使用诸如互相关[55，56]，锁相值[44]，非线性相关系数[57，58]，相位滞后指数[59]，虚构相干[60]，互信息[61]等（请参阅[43]进行回顾）之类的不同方法在头皮信号记录的信号之间计算FC。 这可以在休息状态或诱发的活动中实现。

MNE python [62]，Brainstorm [30]，Brainwave（http://home.kpn.nl/stam7883/brainwave.html）和MATLAB toolbox for FC [63]是开放源代码软件包，能够计算许多 来自M / EEG数据的FC指标。

应用（application）

Many studies reported that scalp magneto/electro-encephalography (M/EEG) connectivity may bring relevant information for example about disrupted functional networks associated epilepsy [65] or with tumors [1]. Yet, the interpretation of connectivity measures from sensor level recordings is not straightforward, as these recordings suffer from a low spatial resolution and are severely corrupted by effects of field spread [4]. For this reason, the past years have witnessed a noticeable increase of interest for functional connectivity at the level of brain sources reconstructed from M/EEG scalp signals. This approach is conceptually very appealing as networks are directly identified in the source space, typically in the neocortex. The advantage is that this approach provides an excellent temporal and very good spatial resolution [3, 4]. This method involves two main steps: i) solving the M/EEG inverse problem to estimate the cortical sources and reconstruct their temporal dynamics and ii) measuring the functional connectivity to assess statistically significant relationships among the temporal dynamics of sources. Several studies showed the usefulness of this technique mainly in brain disorder context such as the epilepsy [66–68]. However, it became trivial to characterize brain networks using approaches based on the graph theory [64].

许多研究报告说，头皮磁/脑电图（M / EEG）的连通性可能带来相关信息，例如有关与癫痫相关的功能网络破坏或与肿瘤相关的信息[1]。然而，从传感器级别的记录中对连通性度量的解释并不简单，因为这些记录的空间分辨率较低，并且受到场扩散影响的严重破坏[4]。由于这个原因，在过去的几年中，人们对从M / EEG头皮信号重建的脑源水平上的功能连通性兴趣显着提高。由于在源空间（通常在新皮层中）直接识别网络，因此该方法在概念上非常有吸引力。优点是这种方法提供了出色的时间分辨率和非常好的空间分辨率[3，4]。该方法包括两个主要步骤：i）解决M / EEG逆问题，以估计皮质源并重建其时间动态； ii）测量功能连通性，以评估源时间动态之间的统计显着关系。几项研究表明，该技术的主要用途是在脑部疾病如癫痫病中[66-68]。然而，使用基于图论的方法来表征大脑网络变得微不足道了[64]。

In this context, EEGNET provides the unique tool that combines the functional connectivity analysis from EEG data with the possibility of characterizing the networks using graph theory based analysis. This possibility of computing the network measures in EEGNET is in great interest for different application such as detecting disrupted nodes/edges properties during brain disorders.

在此背景下，EEGNET提供了将脑电数据的功能连通性分析与基于图论分析的网络特征相结合的独特工具。这种在EEGNET中计算网络度量的可能性对于不同的应用(如在大脑紊乱时检测被破坏的节点/边缘特性)具有重要的意义。

局限与未来（Limitations # and # future # directions）

Further ways for software improvements may include the use of new visualization approaches or improve the existing ones. For instance, EEGNET will be updated to visualize modular partitions of brain networks, allowing for comparisons to well-studied brain networks (e.g., default mode network). The circular view of the brain network used in different tools such as CVU will be also included in EEGNET. Analyzing the dynamics of the identified networks is an important direction of future work, by including algorithms for functional connectivity states for instance [47].

软件改进的其他方法可能包括使用新的可视化方法或改进现有的方法。例如，EEGNET将被更新以可视化大脑网络的模块化分区，允许将其与经过仔细研究的大脑网络(例如，默认模式网络)进行比较。在诸如CVU等不同工具中使用的脑网络的圆形视图也将包含在EEGNET中。通过包括功能连通状态的算法(例如[47])，分析已识别网络的动力学是未来工作的一个重要方向。

The current version of EEGNET does not provide all preprocessing features. Different preprocessing modules will be included in the next version of EEGNET such as bad channel/trials and artifact removal [69]. EEGENT will be improved to support also the different M/EEG format/devices.

当前版本的EEGNET并不提供所有预处理功能。EEGNET的下一个版本将包括不同的预处理模块，例如坏通道/试验和工件去除[69]。EEGENT将得到改进，以支持不同的M/EEG格式/设备。

In the current version of EEGNET, the analysis can be realized on a single subject or on averaged data. In the next version, group analysis will be included in order to study the inter-subject variability and the possible difference between subjects or/and conditions. Concerning the inverse problem algorithms, three different algorithms are integrated in the current version: the Minimum Norm Estimate (MNE), the weighted MNE and the Low resolution Brain Electromagnetic Tomography (LORETA). Descriptions about these methods can be found in [3]. Other algorithms are also expected to be included such as MUSIC-based algorithms [70], the beam-forming algorithm [71] or algorithms based on the maximum entropy [72, 73]. Concerning the connectivity measures, we also expect to add other methods in the next version mainly the effective connectivity methods. Note the EEGNET in its current version is supporting the effective representation (using arrows indicating the direction of the connectivity). In the context of effective connectivity, eConnectome can be the best alternative to use [37].

在EEGNET的当前版本中，可以对单个主题或平均数据进行分析。在下一版本中，将包括组分析，以研究受试者之间的变异性以及受试者或条件之间的可能差异。关于逆问题算法，当前版本中集成了三种不同的算法：最小范数估计（MNE），加权MNE和低分辨率脑电磁层析成像（LORETA）。关于这些方法的描述可以在[3]中找到。预计还将包括其他算法，例如基于MUSIC的算法[70]，波束形成算法[71]或基于最大熵的算法[72、73]。关于连接性措施，我们还希望在下一版本中添加其他方法，主要是有效的连接性方法。请注意，当前版本的EEGNET支持有效表示（使用箭头指示连接方向）。在有效连接的背景下，eConnectome可能是使用的最佳选择[37]。

结语（conclusion）

We have developed a new software tool called EEGNET. The main objective of this tool is to cover the complete processing framework from the M/EEG pre-processing to the identification of the functional brain networks. EEGNET includes mainly the calculation of the functional connectivity between scalp M/EEG signals as well between reconstructed brain sources obtained from the solution of the inverse problem. It also includes the characterization of the brain networks by computing the network measures proposed in the field of graph theory. EEGNET provides user-friendly interactive 2D /3D brain networks visualization.
我们开发了一个新的软件工具，叫做EEGNET。该工具的主要目的是涵盖从M/EEG预处理到识别功能脑网络的完整处理框架。EEGNET主要包括计算头皮M/EEG信号之间的功能连接，以及通过反问题求解得到的重构脑源之间的功能连接。它还包括通过计算图论领域提出的网络测度来表征大脑网络。EEGNET提供用户友好的交互式2D/3D脑网络可视化。