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---
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layout: default
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title: Chapter_01_Getting_Started_with_NFT
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long_title: Chapter_01_Getting_Started_with_NFT
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parent: NFT
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grand_parent: Plugins
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---
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Introduction
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------------
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The Neuroelectromagnetic Forward Head Modeling Toolbox is an open-source
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software toolbox running under MATLAB (The Mathworks, Inc.) for
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generating realistic head models from available data (MRI and/or
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electrode locations) and for solving the forward problem of
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electro-magnetic source imaging. The toolbox includes tools for
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segmenting scalp, skull, cerebrospinal fluid (CSF) and brain tissues
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from T1-weighted magnetic resonance (MR) images. After extracting the
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segmented tissue volumes, mesh generation can be performed. When MR
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images are not available, it is possible to warp a template head model
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to measured electrode locations to obtain a better-fitting head model.
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The toolbox also includes electrode scalp mesh co-registration and
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generation of a uniform source space inside the brain volume for to be
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used in coarse source localization. The Boundary Element Method (BEM) is
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used for the numerical solution of the forward problem. Toolbox
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functions can be called from either a graphic user interface or from the
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command line. Function help messages and a tutorial are included. The
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toolbox is freely available under the GNU Public License for
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noncommercial use and open source development.
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The toolbox uses the following third party tools and libraries for
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segmentation, mesh generation and forward problem solution. The source
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codes for these tools are available.
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1\. ASC - for triangulation of 3D volumes.
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2\. Qslim - for mesh coarsening.
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3\. Matitk - Matlab interface to the ITK image processing toolkit.
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4\. Metu-bem - Boundary Element Method solver.
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The NFT toolbox provides a user interface (UI) for segmentation, mesh
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generation and for creating the numerical head model. It also has a well
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defined MATLAB command-line interface.
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This manual explains how to use the NFT toolbox. The head modeling UI,
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the command line API and the structures are described. An overview of
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the implementation is provided.
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The next section describes the installation of the toolbox. The Getting
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Started section provides an overview of the interface. Head modeling
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from 3D MR images is described next, followed by head modeling from
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template warping. This is followed by a section on forward modeling and
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examples. The [final
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section](Chapter_05_NFT_Commands_and_Functions "wikilink") is a
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summary of all toolbox functions and commands.
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Installation and Configuration
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------------------------------
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This section describes installation and configuration of the NFT
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Toolbox. The following steps are necessary for a proper installation of
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the toolbox:
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1\. Extract or copy the toolbox directory to a suitable place on your
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computer file system.
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2\. The extracted directory will contain m-files, and C++ executables.
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3\. Add the toolbox directory to the MATLAB path. You can use the File →
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SetPath menu item or the addpath() function. Under linux/unix, you may
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add the directory to the MATLABPATH.
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The toolbox can also make use of the Matlab Parallel Processing toolbox
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(if installed) to distribute the computation of the transfer and
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lead-field matrices to multiple processors. To do this, before running
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NFT, the user must simply enter
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\>\> matlabpool(n) % where n is the number of compute nodes available
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In parallel mode, wait bars do not appear while computing the transfer
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and lead-field matrices.
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Getting Started
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---------------
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The toolbox starts by typing
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Neuroelectromagnetic_Forward_Modeling_Toolbox or NFT on command window.
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Main window appears as shown in Figure 1. This window is divided into
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three panels. The first panel is used to select the working folder, and
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to name the subject and the session. The NFM toolbox requires a subject
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folder to be specified at startup. All subject specific output is saved
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into this folder. The filenames are derived from the subject and session
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names entered into this panel. The second panel is the Head modeling
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panel. The head model can either be created from MR images, or a
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template head model can be warped to digitized sensors. The head
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modeling panel provides the following operations when creating a head
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model from MR images:
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![NFT_ui](NFT_ui.png)
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**Image Segmentation**
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Interface for tissue classification from 3D MR Images.
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**Mesh Generation**
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Uses the segmentation results to generate realistic BEM meshes.
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**Source Space Generation**
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Generates a regular grid sources within the brain mesh.
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**Electrode Co-Registration**
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Registers digitized electrode locations to the scalp mesh.
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When generating a template-based head model from digitized electrode
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positions, the only option is Template Warping. The final panel in the
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main menu is for Forward Model Generation. This opens up the Forward
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Model Generation interface which is used to compute the BEM coefficient
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matrix, create the transfer matrices for each sensor, and generate lead
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field matrices for a source distribution.

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