scheduled plugin update

sccn · Jul 25, 2024 · aa83507 · aa83507
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diff --git a/plugins/NFT/index.md b/plugins/NFT/index.md
@@ -8,6 +8,77 @@ nav_order: 3
 ---
 To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/NFT).
 
+# Matlab Toolbox and EEGLAB plugin for Neuroelectromagnetic Forward Head Modeling
+
+![Screenshot 2024-07-25 at 13 28 55](https://github.com/user-attachments/assets/8871c122-dba0-4e1d-a976-7a4a8f6f7c6b)
+
+# What is NFT?
+
+Neuroelectromagnetic Forward Modeling Toolbox (NFT) is a MATLAB toolbox
+for generating realistic head models from available data (MRI and/or
+electrode locations) and for computing numerical solutions for solving
+the forward problem of electromagnetic source imaging (Zeynep Akalin
+Acar & S. Makeig, 2010). NFT includes tools for segmenting scalp, skull,
+cerebrospinal fluid (CSF) and brain tissues from T1-weighted magnetic
+resonance (MR) images. The Boundary Element Method (BEM) is used for the
+numerical solution of the forward problem. After extracting the
+segmented tissue volumes, surface BEM meshes may be generated. When a
+subject MR image is not available, a template head model may be warped
+to 3-D measured electrode locations to obtain an individualized BEM head
+model. Toolbox functions can be called from either a graphic user
+interface (gui) compatible with EEGLAB (sccn.ucsd.edu/eeglab), or from
+the MATLAB command line. Function help messages and a user tutorial are
+included. The toolbox is freely available for noncommercial use and open
+source development under the GNU Public License.
+
+# Why NFT?
+
+The NFT is released under an open source license, allowing researchers
+to contribute and improve on the work for the benefit of the
+neuroscience community. By bringing together advanced head modeling and
+forward problem solution methods and implementations within an easy to
+use toolbox, the NFT complements EEGLAB, an open source toolkit under
+active development. Combined, NFT and EEGLAB form a freely available EEG
+(and in future, MEG) source imaging solution.
+
+The toolbox implements the major aspects of realistic head modeling and
+forward problem solution from available subject information:
+
+1.  Segmentation of T1-weighted MR images: The preferred method of
+    generating a realistic head model is to use a 3-D whole-head
+    structural MR image of the subject's head. The toolbox can generate
+    a segmentation of scalp, skull, CSF and brain tissues from a
+    T1-weighted image.
+
+2.  High-quality BEM meshes: The accuracy of the BEM solution depends on
+    the quality of the underlying mesh that models tissue
+    conductance-change boundaries. To avoid numerical instabilities, the
+    mesh must be topologically correct with no self-intersections. It
+    should represent the surface using high-quality elements while
+    keeping the number of elements as small as possible. The NFT can
+    create high-quality linear surface BEM meshes from the head
+    segmentation.
+
+3.  Warping a template head model: When a whole-head structural MR image
+    of the subject is not available, a semi-realistic head model can be
+    generated by warping a standard template BEM mesh to the digitized
+    electrode coordinates (instead of vice versa).
+
+4.  Registration of electrode positions with the BEM mesh: The digitized
+    electrode locations and the BEM mesh must be aligned to compute
+    accurate forward problem solutions and lead field matrices.
+
+5.  Accurate high-performance forward problem solution: The NFT uses a
+    high-performance BEM implementation from the open source METU-FP
+    Toolkit for bioelectromagnetic field computations.
+
+# Required Resources
+
+Matlab 7.0 or later running under any operating system (Linux, Windows).
+A large amount of RAM is useful - at least 2 GB (4-8 GB recommended for
+forward problem solution of realistic head models). The Matlab Image
+Processing toolbox is also recommended.
+
 Pre-compiled binaries for the following 3rd party programs are distributed
 within the NFT toolbox for convinience of the users. The binaries are compiled
 for 32 and 64 bit Linux distributions.
@@ -31,3 +102,21 @@ MATITK: Matlab and ITK
 homepage: http://www.sfu.ca/~vwchu/matitk.html
 
 Note: The MATITK shared libraries are installed in the 'mfiles' directory.
+
+# Download
+
+To download the NFT, go to the [NFT download
+page](http://sccn.ucsd.edu/nft/).
+
+# NFT User's Manual
+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)
+
+Creation and documentation by: Zeynep Akalin Acar, Project Scientist, zeynep@sccn.ucsd.edu
+
+# NFT Reference Paper
+
+Zeynep Akalin Acar & Scott Makeig, [Neuroelectromagnetic Forward Head
+Modeling
+Toolbox](http://sccn.ucsd.edu/%7Escott/pdf/Zeynep_NFT_Toolbox10.pdf).
+<em>Journal of Neuroscience Methods</em>, 2010
+
diff --git a/plugins/NIMA/index.md b/plugins/NIMA/index.md
@@ -8,6 +8,11 @@ nav_order: 26
 ---
 To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/NIMA).
 
+![P159_separatealpha.png](images/P159_separatealpha.png)
+
+The NIMA EEGLAB plugin
+-------------------------------------------------------------
+
 NIMA stands for Nima's Images from Measure-projection Analysis. Measure
 Projection Toolbox (MPT) is a published method (Bigdely-Shamlo et al.,
 2013), and for his wiki page see [this
@@ -25,11 +30,8 @@ What you can do with the optional inputs (12/07/2018 updated)
 -   Specifying which MRI image and blob/voxel-cluster projections to
     show.
 
-![P159_separatealpha.png](images/P159_separatealpha.png)
-
 GUI, Blobs, and Voxels
 ----------------------
-
 GUI image can be seen in the screenshot below. This visualization works
 on 3-D Gaussian-blurred dipole locations, called (probabilistic) *dipole
 density*, which requires two parameters to determine the spatial
@@ -91,4 +93,4 @@ FWHM = 8 mm, number of sigma to truncate Gaussian = 3. Top row, voxel
 plot. Bottom row, blob plot. From left to right, Alpha = 0.1, 0.3, 0.5,
 0.7, 0.9.
 
-![Alphacomparison.png](images/Alphacomparison.png)
+![Alphacomparison.png](images/Alphacomparison.png)
diff --git a/plugins/PACTools/index.md b/plugins/PACTools/index.md
@@ -8,11 +8,6 @@ nav_order: 17
 ---
 To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/PACTools).
 
-[![GitHub stars](https://img.shields.io/github/stars/sccn/PACTools?color=%235eeb34&logo=GithUb&logoColor=%23fafafa)](https://github.com/sccn/PACTools/stargazers)
-[![GitHub forks](https://img.shields.io/github/forks/sccn/PACTools?color=%23b3d9f5&logo=GitHub)](https://github.com/sccn/PACTools/network)
-[![GitHub issues](https://img.shields.io/github/issues/sccn/PACTools?color=%23fa251e&logo=GitHub)](https://github.com/sccn/PACTools/issues)
-![Twitter Follow](https://img.shields.io/twitter/follow/eeglab2?style=social)
-
 # EEGLAB Event Related PACTools
 The Event Related PACTools (PACTools) is an EEGLAB plug-in to compute phase-amplitude coupling in single subject data. 
 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).