R-universe - dipterix (Dipterix Wang)

threeBrain - Your Advanced 3D Brain Visualization

A fast, interactive cross-platform, and easy to share 'WebGL'-based 3D brain viewer that visualizes 'FreeSurfer' and/or 'AFNI/SUMA' surfaces. The viewer widget can be either standalone or embedded into 'R-shiny' applications. The standalone version only require a web browser with 'WebGL2' support (for example, 'Chrome', 'Firefox', 'Safari'), and can be inserted into any websites. The 'R-shiny' support allows the 3D viewer to be dynamically generated from reactive user inputs. Please check the publication by Wang, Magnotti, Zhang, and Beauchamp (2023, <doi:10.1523/ENEURO.0328-23.2023>) for electrode localization. This viewer has been fully adopted by 'RAVE' <https://rave.wiki>, an interactive toolbox to analyze 'iEEG' data by Magnotti, Wang, and Beauchamp (2020, <doi:10.1016/j.neuroimage.2020.117341>). Please check citation("threeBrain") for details.

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brainecogelectroencephalographyieeglocalizationmri-brainneuroimagingtemplate-brains

8.47 score 49 stars 2 dependents 48 scripts 657 downloads

dipsaus - A Dipping Sauce for Data Analysis and Visualizations

Works as an "add-on" to packages like 'shiny', 'future', as well as 'rlang', and provides utility functions. Just like dipping sauce adding flavors to potato chips or pita bread, 'dipsaus' for data analysis and visualizations adds handy functions and enhancements to popular packages. The goal is to provide simple solutions that are frequently asked for online, such as how to synchronize 'shiny' inputs without freezing the app, or how to get memory size on 'Linux' or 'MacOS' system. The enhancements roughly fall into these four categories: 1. 'shiny' input widgets; 2. high-performance computing using the 'future' package; 3. modify R calls and convert among numbers, strings, and other objects. 4. utility functions to get system information such like CPU chip-set, memory limit, etc.

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cpp

8.41 score 15 stars 4 dependents 118 scripts 902 downloads

filearray - File-Backed Array for Out-of-Memory Computation

Stores large arrays in files to avoid occupying large memories. Implemented with super fast gigabyte-level multi-threaded reading/writing via 'OpenMP'. Supports multiple non-character data types (double, float, complex, integer, logical, and raw).

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arraybig-datamemory-mapout-of-memoryoutofmemorycpp

6.56 score 17 stars 4 dependents 12 scripts 721 downloads

ieegio - File IO for Intracranial Electroencephalography

Integrated toolbox supporting common file formats used for intracranial Electroencephalography (iEEG) and deep-brain stimulation (DBS) study.

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bci2000brainbrainvisiondbsedfelectrophysiologyephysfreesurferieegneuroimagingneuroscienceniftinwb-format

6.13 score 1 stars 2 dependents 16 scripts 181 downloads

readNSx - Read 'Blackrock-Microsystems' Files ('NEV', 'NSx')

Loads 'Blackrock' <https://blackrockneurotech.com> neural signal data files into the memory, provides utility tools to extract the data into common formats such as plain-text 'tsv' and 'HDF5'.

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data-formatsdbiieegieeg-dataneuroscience-datacpp

5.91 score 3 stars 3 dependents 10 scripts 240 downloads

shidashi - A Shiny Dashboard Template Modular System with Chat Bot Support

A template dashboard system with AI agent integrated. Comes with default themes that can be customized. Developers can upload modified templates on 'Github', and users can easily download templates with 'RStudio' project wizard. The key features of the default template include light and dark theme switcher, resizing graphs, synchronizing inputs across sessions, new notification system, fancy progress bars, and card-like flip panels with back sides, as well as various of 'HTML' tool widgets.

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5.56 score 11 stars 1 dependents 73 scripts 634 downloads

ravetools - Signal and Image Processing Toolbox for Analyzing Intracranial Electroencephalography Data

Implemented fast and memory-efficient Notch-filter, Welch-periodogram, discrete wavelet spectrogram for minutes of high-resolution signals, fast 3D convolution, image registration, 3D mesh manipulation; providing fundamental toolbox for intracranial Electroencephalography (iEEG) pipelines. Documentation and examples about 'RAVE' project are provided at <https://rave.wiki>, and the paper by John F. Magnotti, Zhengjia Wang, Michael S. Beauchamp (2020) <doi:10.1016/j.neuroimage.2020.117341>; see 'citation("ravetools")' for details.

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fftw3cpp

5.45 score 3 stars 2 dependents 31 scripts 650 downloads

ravepipeline - Reproducible Pipeline Infrastructure for Neuroscience

Defines the underlying pipeline structure for reproducible neuroscience, adopted by 'RAVE' (reproducible analysis and visualization of intracranial electroencephalography); provides high-level class definition to build, compile, set, execute, and share analysis pipelines. Both R and 'Python' are supported, with 'Markdown' and 'shiny' dashboard templates for extending and building customized pipelines. See the full documentations at <https://rave.wiki>; to cite us, check out our paper by Magnotti, Wang, and Beauchamp (2020, <doi:10.1016/j.neuroimage.2020.117341>), or run citation("ravepipeline") for details.

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5.40 score 2 dependents 42 scripts 561 downloads

rpyANTs - An Alternative Advanced Normalization Tools ('ANTs')

Provides portable access from 'R' to biomedical image processing toolbox 'ANTs' by Avants et al. (2009) <doi:10.54294/uvnhin> via seamless integration with the 'Python' implementation 'ANTsPy'. Allows biomedical images to be processed in 'Python' and analyzed in 'R', and vice versa via shared memory. See 'citation("rpyANTs")' for more reference information.

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antsbrainieegmrineuroimagingnormalizationpythonregistration

4.91 score 3 stars 3 dependents 7 scripts 264 downloads

rpymat - Easy to Configure an Isolated 'Python' Environment

Aims to create a single isolated 'Miniconda' and 'Python' environment for reproducible pipeline scripts. The package provides utilities to run system command within the 'conda' environment, making it easy to install, launch, manage, and stop 'Jupyter-lab'.

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4.56 score 1 stars 5 dependents 12 scripts 327 downloads

bidsr - A Brain Imaging Data Structure ('BIDS') Parser

Parse and read the files that comply with the brain imaging data structure, or 'BIDS' format, see the publication from Gorgolewski, K., Auer, T., Calhoun, V. et al. (2016) <doi:10.1038/sdata.2016.44>. Provides query functions to extract and check the 'BIDS' entity information (such as subject, session, task, etc.) from the file paths and suffixes according to the specification. The package is developed and used in the reproducible analysis and visualization of intracranial electroencephalography, or 'RAVE', see Magnotti, J. F., Wang, Z., and Beauchamp, M. S. (2020) <doi:10.1016/j.neuroimage.2020.117341>; see 'citation("bidsr")' for details and attributions.

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4.48 score 1 stars 2 dependents 5 scripts 256 downloads

ravedash - Dashboard for Reproducible Visualization of 'iEEG'

Dashboard system to display the analysis results produced by 'RAVE', Magnotti J.F., Wang Z., Beauchamp M.S. (2020), <doi:10.1016/j.neuroimage.2020.117341>; see <https://rave.wiki> for more documentations. Provides infrastructure to integrate customized analysis pipelines into dashboard modules, including file structures, front-end widgets, and event handlers; see <https://github.com/rave-ieeg/rave-pipelines> for implementation details.

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4.38 score 1 stars 60 scripts 44 downloads

ravecore - Core File Structures and Workflows for 'RAVE'

Defines storage standard for Read, process, and analyze intracranial electroencephalography and deep-brain stimulation in 'RAVE', a reproducible framework for analysis and visualization of iEEG by Magnotti, Wang, and Beauchamp, (2020, <doi:10.1016/j.neuroimage.2020.117341>). Supports brain imaging data structure (BIDS) <https://bids.neuroimaging.io> and native file structure to ingest signals from 'Matlab' data files, hierarchical data format 5 (HDF5), European data format (EDF), BrainVision core data format (BVCDF), or BlackRock Microsystem (NEV/NSx); process images in Neuroimaging informatics technology initiative (NIfTI) and 'FreeSurfer' formats, providing brain imaging normalization to template brain, facilitating 'threeBrain' package for comprehensive electrode localization via 'YAEL' (your advanced electrode localizer) by Wang, Magnotti, Zhang, and Beauchamp (2023, <doi:10.1523/ENEURO.0328-23.2023>).

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4.38 score 1 dependents 1 scripts 698 downloads

raveio - File-System Toolbox for RAVE Project

Includes multiple cross-platform read/write interfaces for 'RAVE' project. 'RAVE' stands for "R analysis and visualization of human intracranial electroencephalography data". The whole project aims at providing powerful free-source package that analyze brain recordings from patients with electrodes placed on the cortical surface or inserted into the brain. 'raveio' as part of this project provides tools to read/write neurophysiology data from/to 'RAVE' file structure, as well as several popular formats including 'EDF(+)', 'Matlab', 'BIDS-iEEG', and 'HDF5', etc. Documentation and examples about 'RAVE' project are provided at <https://rave.wiki>, and the paper by John F. Magnotti, Zhengjia Wang, Michael S. Beauchamp (2020) <doi:10.1016/j.neuroimage.2020.117341>. For applications such as electrode localization, please check Zhengjia Wang, John F. Magnotti, Xiang Zhang, Michael S. Beauchamp (2023) <doi:10.1523/ENEURO.0328-23.2023>, or see 'citation("raveio")' for details.

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4.07 score 9 stars 66 scripts 148 downloads

rave - R Analysis and Visualization of 'ECOG/iEEG' Data

R Analysis and Visualization of 'ECOG/iEEG' Data RAVE provides cross-platform analysis and visualizations for neuroscience 'ECoG/iEEG' data by "just clicking buttons". This package include: (a) pre-processing of 'ECoG/iEEG' raw data into 'HDF5' format (a cross-platform data format that can be used for R, Matlab, Python, Java and C/C++); (b) 'ECoG/iEEG' epoch and visualizations in real time; (c) Users can write customized analysis as R packages and easily insert into RAVE for interactive visualizations. (d) 3D viewers driven by 'WebGL'.

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3.92 score 25 stars 11 scripts

spfda - Function-on-Scalar Regression with Group-Bridge Penalty

Implements a group-bridge penalized function-on-scalar regression model proposed by Wang et al. (2023) <doi:10.1111/biom.13684>, to simultaneously estimate functional coefficient and recover the local sparsity.

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function-on-scalar-regressionregularization-methodssparse

2.70 score 1 scripts 578 downloads