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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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# Matlab Toolbox and EEGLAB plugin for Neuroelectromagnetic Forward Head Modeling
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# What is NFT?
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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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# Why NFT?
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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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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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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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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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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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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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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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# Required Resources
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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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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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homepage: http://www.sfu.ca/~vwchu/matitk.html
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Note: The MATITK shared libraries are installed in the 'mfiles' directory.
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# Download
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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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# NFT User's Manual
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See the tutorial section for more information. [Click here to download the NFT User Manual as a PDF book](https://github.com/user-attachments/files/16383465/NFT_Tutorial.pdf)
The Event Related PACTools (PACTools) is an EEGLAB plug-in to compute phase-amplitude coupling in single subject data.
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In addition to traditional methods to compute PAC, the plugin include the Instantaneuous and Event-Related implementation of the Mutual Information Phase-Amplitude Coupling Method (MIPAC) (see Martinez-Cancino et al 2019).
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