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title : NFT
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long_title : NFT
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parent : Plugins
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- categories : plugins
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has_children : true
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nav_order : 3
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---
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To view the plugin source code, please visit the plugin's [ GitHub repository] ( https://github.com/sccn/NFT ) .
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- ### Open Source Matlab Toolbox for Neuroelectromagnetic Forward Head Modeling
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+ Pre-compiled binaries for the following 3rd party programs are distributed
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+ within the NFT toolbox for convinience of the users. The binaries are compiled
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+ for 32 and 64 bit Linux distributions.
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- ![ right] ( NFTsmall.jpg " wikilink ")
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+ All of these programs have opensource licenses and provide full source-code.
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+ Please visit home-pages of individual programs for more information on usage,
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+ source-code and license information.
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- ### What is NFT?
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+ ASC: Adaptive skeleton climbing
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+ homepage: http://www.cse.cuhk.edu.hk/~ttwong/papers/asc/asc.html
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- Neuroelectromagnetic Forward Modeling Toolbox (NFT) is a MATLAB toolbox
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- for generating realistic head models from available data (MRI and/or
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- electrode locations) and for computing numerical solutions for solving
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- the forward problem of electromagnetic source imaging (Zeynep Akalin
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- Acar & S. Makeig, 2010). NFT includes tools for segmenting scalp, skull,
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- cerebrospinal fluid (CSF) and brain tissues from T1-weighted magnetic
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- resonance (MR) images. The Boundary Element Method (BEM) is used for the
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- numerical solution of the forward problem. After extracting the
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- segmented tissue volumes, surface BEM meshes may be generated. When a
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- subject MR image is not available, a template head model may be warped
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- to 3-D measured electrode locations to obtain an individualized BEM head
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- model. Toolbox functions can be called from either a graphic user
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- interface (gui) compatible with EEGLAB (sccn.ucsd.edu/eeglab), or from
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- the MATLAB command line. Function help messages and a user tutorial are
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- included. The toolbox is freely available for noncommercial use and open
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- source development under the GNU Public License.
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+ QSLIM: Quadric-based surface simplification
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+ homepage: http://mgarland.org/software/qslim.html
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- ### Why NFT?
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+ BEM_MATRIX: The METU-FP Toolkit
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+ homepage: http://www.eee.metu.edu.tr/metu-fp/
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- The NFT is released under an open source license, allowing researchers
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- to contribute and improve on the work for the benefit of the
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- neuroscience community. By bringing together advanced head modeling and
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- forward problem solution methods and implementations within an easy to
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- use toolbox, the NFT complements EEGLAB, an open source toolkit under
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- active development. Combined, NFT and EEGLAB form a freely available EEG
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- (and in future, MEG) source imaging solution.
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+ PROCMESH: Mesh correction and processing. No web page yet. Please contact NFT developers for source code.
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- The toolbox implements the major aspects of realistic head modeling and
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- forward problem solution from available subject information:
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+ MATITK: Matlab and ITK
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+ homepage: http://www.sfu.ca/~vwchu/matitk.html
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- 1 . Segmentation of T1-weighted MR images: The preferred method of
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- generating a realistic head model is to use a 3-D whole-head
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- structural MR image of the subject's head. The toolbox can generate
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- a segmentation of scalp, skull, CSF and brain tissues from a
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- T1-weighted image.
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-
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- 2 . High-quality BEM meshes: The accuracy of the BEM solution depends on
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- the quality of the underlying mesh that models tissue
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- conductance-change boundaries. To avoid numerical instabilities, the
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- mesh must be topologically correct with no self-intersections. It
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- should represent the surface using high-quality elements while
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- keeping the number of elements as small as possible. The NFT can
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- create high-quality linear surface BEM meshes from the head
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- segmentation.
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-
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- 3 . Warping a template head model: When a whole-head structural MR image
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- of the subject is not available, a semi-realistic head model can be
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- generated by warping a standard template BEM mesh to the digitized
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- electrode coordinates (instead of vice versa).
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-
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- 4 . Registration of electrode positions with the BEM mesh: The digitized
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- electrode locations and the BEM mesh must be aligned to compute
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- accurate forward problem solutions and lead field matrices.
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-
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- 5 . Accurate high-performance forward problem solution: The NFT uses a
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- high-performance BEM implementation from the open source METU-FP
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- Toolkit for bioelectromagnetic field computations.
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-
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- ### Required Resources
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-
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- Matlab 7.0 or later running under any operating system (Linux, Windows).
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- A large amount of RAM is useful - at least 2 GB (4-8 GB recommended for
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- forward problem solution of realistic head models). The Matlab Image
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- Processing toolbox is also recommended.
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-
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- ### NFT Reference Paper
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-
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- Zeynep Akalin Acar & Scott Makeig, [ Neuroelectromagnetic Forward Head
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- Modeling
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- Toolbox] ( http://sccn.ucsd.edu/%7Escott/pdf/Zeynep_NFT_Toolbox10.pdf ) .
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- <em >Journal of Neuroscience Methods</em >, 2010
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-
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- Download
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- --------
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-
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- To download the NFT, go to the [ NFT download
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- page] ( http://sccn.ucsd.edu/nft/ ) .
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-
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- NFT User's Manual
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- -----------------
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-
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- - [ Chapter 01: Getting Started with NFT] ( Chapter_01_Getting_Started_with_NFT " wikilink ")
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- - [ Chapter 02: Head Modeling from MR Images] ( Chapter_02_Head_Modeling_from_MR_Images " wikilink ")
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- - [ Chapter 03: Forward Model Generation] ( Chapter_03_Forward_Model_Generation " wikilink ")
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- - [ Chapter 04: NFT Examples] ( Chapter_04_NFT_Examples " wikilink ")
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- - [ Chapter 05: NFT Commands and Functions] ( Chapter_05_NFT_Commands_and_Functions " wikilink ")
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- - [ Appendix A: BEM Mesh Format] ( NFT_Appendix_A )
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- - [ Appendix B: Function Reference] ( NFT_Appendix_B )
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- - [ Appendix C: Effect of brain-to-skull conductivity ratio estimate] ( NFT_Appendix_C )
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-
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-
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- - [ Click here to download the NFT User Manual as a PDF book] ( NFT_Tutorial.pdf )
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-
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- <div align =right >
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-
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- Creation and documentation by:
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-
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- Zeynep Akalin Acar
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-
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- Project Scientist
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-
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-
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-
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- </div >
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+ Note: The MATITK shared libraries are installed in the 'mfiles' directory.
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