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http://www.nitrc.org/projects/eeglab/

Interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. First developed on Matlab 5.3 under Linux, EEGLAB runs on Matlab v5 and higher under Linux, Unix, Windows, and Mac OS X (Matlab 7+ recommended). EEGLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and interactively process their high-density EEG and other dynamic brain data using independent component analysis (ICA) and/or time/frequency analysis (TFA), as well as standard averaging methods. EEGLAB also incorporates extensive tutorial and help windows, plus a command history function that eases users'' transition from GUI-based data exploration to building and running batch or custom data analysis scripts. EEGLAB offers a wealth of methods for visualizing and modeling event-related brain dynamics, both at the level of individual EEGLAB ''datasets'' and/or across a collection of datasets brought together in an EEGLAB ''studyset.'' For experienced Matlab users, EEGLAB offers a structured programming environment for storing, accessing, measuring, manipulating and visualizing event-related EEG data. For creative research programmers and methods developers, EEGLAB offers an extensible, open-source platform through which they can share new methods with the world research community by publishing EEGLAB ''plug-in'' functions that appear automatically in the EEGLAB menu of users who download them. For example, novel EEGLAB plug-ins might be built and released to ''pick peaks'' in ERP or time/frequency results, or to perform specialized import/export, data visualization, or inverse source modeling of EEG, MEG, and/or ECOG data. EEGLAB Features * Graphic user interface * Multiformat data importing * High-density data scrolling * Defined EEG data structure * Open source plug-in facility * Interactive plotting functions * Semi-automated artifact removal * ICA & time/frequency transforms * Many advanced plug-in toolboxes * Event & channel location handling * Forward/inverse head/source modeling

Proper citation: EEGLAB (RRID:SCR_007292) Copy   

•   Source: SciCrunch Registry

http://sccn.ucsd.edu/

Computational neuroscience center that observes and models how functional activities in multiple brain areas interact dynamically to support human cognition, creativity and social interaction. Center research involves development computational methods and software, experimental methods and equipment, collection and analysis of human cognitive experiments, and collaborations to analyze data collected by other groups in such experiments. The Center has a 72-channel EEG recording system customized for use in the fMRI environment, and a very-high density Biosemi Active Two active-electrode EEG system, rapidly configurable either as a 256-channel system for a single subject or as two 136-channel systems for recording from two subjects simultaneously. In addition, UCSD now has a 306-channel MEG plus 128-channel EEG system (Neuromag/Elektra). Projects in the Center include studies of human cognitive processes including attention and memory, role of the anterior/posterior cingulate, time perception and emotional expression. Data acquisition includes high-density EEG, concurrent EEG and fMRI recording and analysis, and face video processing. Current analysis approaches include independent component and time-frequency analysis.

Proper citation: Swartz Center for Computational Neuroscience (RRID:SCR_001933) Copy   

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http://sccn.ucsd.edu/wiki/NFT

A MATLAB Toolbox for generating realistic head models from available data (MRI and/or electrode locations), for computing numerical solutions for the forward problem of electromagnetic source imaging and for single dipole source localization. The 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) and Finite Element Method (FEM) are used for the numerical solution of the forward problem. When a subject MR image is not available a template head model can be warped to measured electrode locations to obtain an individualized head model. Toolbox functions may be called either from a graphic user interface compatible with EEGLAB or from the MATLAB command line.

Proper citation: NFT (RRID:SCR_002450) Copy   

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http://sccn.ucsd.edu/wiki/SIFT

A GUI-enabled EEGLAB plugin for modeling and visualizing dynamical interactions between electrophysiological signals (EEG, ECoG, MEG, etc), preferably after transforming signals into the source domain. The toolbox consists of four modules: (1) Data Preprocessing, (2) Model Fitting and Connectivity Estimation, (3) Statistical Analysis, (4) Visualization, with a fifth Group Analysis module in development. Module 2 currently includes several adaptive multivariate autoregressive modeling (AMVAR) algorithms, including segmentation AMVAR and Kalman filtering. This subsequently allows the user to validate the model and estimate (in the time-frequency domain) a wide range of multivariate Granger-causal and coherence measures published to date. Module 3 includes routines for parametric and non-parametric significance testing. Module 4 contains routines for interactive visualization of dynamical interactions across time, frequency and anatomical source location.

Proper citation: Source Information Flow Toolbox (RRID:SCR_002561) Copy   

•   Source: SciCrunch Registry

https://www.fieldtriptoolbox.org

Software toolbox for analysis of MEG, EEG, and other electrophysiological data. Used by experimental neuroscientists.

Proper citation: FieldTrip (RRID:SCR_004849) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/

Software repository for comparing structural (MRI) and functional neuroimaging (fMRI, PET, EEG, MEG) software tools and resources. NITRC collects and points to standardized information about structural or functional neuroimaging tool or resource.

Proper citation: NeuroImaging Tools and Resources Collaboratory (NITRC) (RRID:SCR_003430) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/erpwavelab

A toolbox developed for multi-channel time-frequency analysis of event related activity of EEG and MEG data. It provides tools for data analysis and visualization of the most commonly used measures of time-frequency transformed event related data as well as data decomposition through non-negative matrix and multi-way (tensor) factorization. The decompositions provided can accommodate additional dimensions like subjects, conditions or repeats and as such they are perfected for group analysis. The toolbox enables tracking of phase locked activity from one channel-time-frequency instance to another as well as tools for artifact rejection in the time-frequency domain.

Proper citation: ERPwavelab (RRID:SCR_014106) Copy   

•   Source: SciCrunch Registry

http://fmri.uib.no/index.php?option=com_content&view=article&id=53&Itemid=78

Software package as a set of Matlab tools which allow the correction of fMRI-related gradient artifacts from EEG data. Designed to work within the EEGLAB environment, providing a GUI to remove fMRI gradient artifacts from the EEG. Can also be used from the Matlab command line, providing expert users with the ability to use them in custom scripts.

Proper citation: The Bergen fMRI Toolbox Plugin for EEGLab (RRID:SCR_016335) Copy   

•   Source: SciCrunch Registry

https://sourceforge.net/projects/bva-io/

Software package for interfacing the Brain Vision Analyser data files (load/save) for ongoing development of Matlab routines . This package is also compatible with the EEGLAB software, and may be uncompressed in the plugin folder of this software.

Proper citation: BVA import/export EEGLAB plugin (RRID:SCR_016333) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/paa/

Software application for Period Amplitude Analysis (PAA) for slow wave detection using eeglab.

Proper citation: PeriodAmplitudeAnalysis (RRID:SCR_027418) Copy   

•   Source: SciCrunch Registry

http://neuronalarchitects.com/index.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 17, 2016. A C#.NET/C++.NET 4.0 API multi-threaded, parallel class library with CUDA kernels for EEG predictive analytics gleaned from the ModelMaker 2 application. This web service and component library offers functionality to do univariate and multivariate nonlinear time series and frequency based predictive analysis for EEG / Ecog / MEG signals for gaming applications. Neural Maestro works with both EEGLab / BCILab and eConnectome as well as other MATLAB and R packages. It enables one to build highly sophisticated neuroscience applications with little effort in Windows applications.

Proper citation: Neural Maestro (RRID:SCR_001563) Copy   

•   Source: SciCrunch Registry

http://headit.ucsd.edu

Platform for sharing, download, and re-analysis or meta-analysis of sophisticated, fully annotated, human electrophysiological data sets. It uses EEG Study Schema (ESS) files to provide task, data collection, and subject metadata, including Hierarchical Event Descriptor (HED) tag descriptions of all identified experimental events. Visospatial task data also available from, http://sccn.ucsd.edu/eeglab/data/headit.html: A 238-channel, single-subject EEG data set recorded at the Swartz Center, UCSD, by Arnaud Delorme, Julie Onton, and Scott Makeig is al.

Proper citation: HeadIT (RRID:SCR_005657) Copy   

•   Source: SciCrunch Registry

http://www2.hu-berlin.de/eyetracking-eeg

A plugin for the open-source MATLAB toolbox EEGLAB developed with the goal to facilitate integrated analyses of electrophysiological and oculomotor data. The plugin parses, imports, and synchronizes simultaneously recorded eye tracking data and adds it as extra channels to the EEG. Saccades and fixations can be imported from the eye tracking raw data or detected with an adaptive velocity-based algorithm. Eye movements are then added as new time-locking events to EEGLAB's event structure, allowing easy saccade- and fixation-related EEG analysis (e.g., fixation-related potentials, FRPs). Alternatively, EEG data can be aligned to stimulus onsets and analyzed according to oculomotor behavior (e.g. pupil size, microsaccades) in a given trial. Saccade-related ICA components can be objectively identified based on their covariance with the electrically independent eye tracker. All functions can be accessed via EEGLAB's GUI or called from the command line.

Proper citation: EYE-EEG (combined eye-tracking & EEG) (RRID:SCR_012903) Copy   

•   Source: SciCrunch Registry

http://visual.cs.utsa.edu/eegvis

A MATLAB toolbox for exploration of multi-channel EEG and other large array-based data sets using multi-scale drill-down techniques. The toolbox can be used directly in MATLAB at any stage in a user's processing pipeline, as a plug in for EEGLAB, or as a standalone precompiled application without MATLAB running. EEGVIS and its supporting packages are freely available under the GNU general public license. The toolbox also supplies a number of extensible base classes for users who wish to develop their own visualizations.

Proper citation: EEGVIS (RRID:SCR_009569) Copy   

•   Source: SciCrunch Registry

http://www.ncbcs.org/biositemaps/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 27,2023. Biositemaps represent a mechanism for computational biologists and bio-informaticians to openly broadcast and retrieve meta-data about biomedical data, tools and services (i.e., biomedical resources) over the Internet. All Institutions with an interest in biomedical research can publish a biositemap.rdf file on their Internet site. The technology, developed by the Biositemaps Working Group of the NIH Roadmap National Centers of Biomedical Computing (NCBC), addresses (i) locating, (ii) querying, (iii) composing or combining, and (iv) mining biomedical resources. Each site which intends to contribute to the inventory instantiates a file on its Internet site biositemap.rdf which conforms to a defined RDF schema and uses concepts from the Biomedical Resource Ontology to describe the resources. Each biositemap.rdf file is simply a list of controlled metadata about resources (software tools, databases, material resources) that your organization uses or believes are important to biomedical research. The key enabling technologies are the Information Model (IM) which is the list of metadata fields about each resource (resource_name, description, contact_person, resource_type,...) and the Biomedical Resource Ontology (BRO) which is a controlled terminology for the resource_typeand which is used to improve the sensitivity and specificity of web searches. Biositemaps blend the features of Sitemaps (enabling efficient web-content exploration) and RSS Feeds (a mechanism for wide and effective news dissemination). As a hybrid between Sitemaps and RSS feeds, the Biositemap infrastructure facilitates a decentralized, portable, extensible and computationally tractable generation and consumption of meta-data about existent, revised and new resources for biomedical computation. Web browsers, crawlers and robots can discover, accumulate, process, integrate and deliver Biositemaps content to (human or machine) users in a variety of graphical, tabular, computational formats. Biositemaps content allows such web browsers to pool resource-associated metadata from disparate and diverse sites and present it to the user in an integrated fashion. The Biositemaps protocol provides clues, information and directives for all Biositemap web harvesters that point to the existence and content of such biomedical resources at different sites.

Proper citation: Biositemaps (RRID:SCR_001976) Copy   

•   Source: SciCrunch Registry

http://sccn.ucsd.edu/wiki/MPT

This toolbox is an EEGLAB plugin for performing Measure Projection Analysis. Measure Projection Analysis (MPA) is a novel probabilistic multi-subject inference method that overcomes EEG Independent Component (IC) clustering issues by abandoning the notion of distinct IC clusters. Instead, it searches voxel by voxel for brain regions having event-related IC process dynamics that exhibit statistically significant consistency across subjects and/or sessions as quantified by the values of various EEG measures. Local-mean EEG measure values are then assigned to all such locations based on a probabilistic model of IC localization error and inter-subject anatomical and functional differences.

Proper citation: Measure Projection Toolbox (RRID:SCR_002429) Copy   

•   Source: SciCrunch Registry

http://download.ant-neuro.com/matlab/

Software as an EEGLAB tool used for LIBEEP recordings.

Proper citation: Libeep EEGLAB plugin (RRID:SCR_016334) Copy   

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https://github.com/howisonlab/softcite-dataset

Gold standard dataset of software mentions in research publications. Provides dataset of annotated software mentions from full text academic literature in biomedicine and economics directly converted from published PDFs with reproducible infrastructure. Includes provenance, and is formatted for immediately usefulness in NLP. Useful for supervised learning at scale.

Proper citation: SoftCite (RRID:SCR_024411) Copy   

•   Source: SciCrunch Registry

http://sccn.ucsd.edu/wiki/BCILAB

Open Source MATLAB toolbox and EEGLAB plugin for the design, prototyping, testing, experimentation with, and evaluation of Brain-Computer Interfaces (BCIs), and other systems in the same computational framework. It facilitates the design and development of new methods for cognitive state estimation and their use in both offline data analysis and real-time applications. BCILAB includes an easily extensible collection of currently over 100 methods from the literature (covering signal processing, machine learning and BCI-specific methods). Aside from supporting advanced BCI research, a special aim of BCILAB is to facilitate the adoption of machine learning and advanced statistical modeling for functional neuroimaging purposes in tandem with the EEGLAB platform. The toolbox offers multiple different interfaces which link to the same backend functionality, including a GUI, scripting support (MATLAB-based), APIs for real-time processing, and a variety of extension component interfaces. MATLAB programming is not strictly necessary, as most BCILAB features can be accessed from the GUI, although it is required for batch scripting and custom extensions. The strength of MATLAB-based software lies in its resources for leading-edge scientific computing, as well as in the good support for rapid prototyping, but BCI systems developed in it can be used for real-time out-of-lab experimentation, and can in principle be deployed without the need for a MATLAB license. However, due to the complexity and overhead of the MATLAB environment, the system is best used as a research platform, and not as a product development environment -- end-user software is ideally re-implemented in a compiled language, after a suitable approach has been identified and extensively tested. The process of identifying and testing an approach involves more than just computation, but also data exploration and investigation - an area which is helped by the deep integration with the EEGLAB platform. In the future, this integration will be further strengthened, bringing rich statistical learning and signal processing into routine EEG analysis workflows. The toolbox has been developed by C. Kothe at the Swartz Center, inspired by the preceding PhyPA BCI toolbox created by C. Kothe and T. Zander at the Chair for Human-Machine Systems, Berlin Institute of Technology.

Proper citation: BCILAB (RRID:SCR_007013) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/dusk2dawn/

Software plugin for 'EEGLAB' MATLAB toolbox, which allows to whole-night sleep EEG using Artifact Subspace Reconstruction (ASR). Used to implement Artifact Subspace Reconstruction cleaning with whole-night sleep EEG. Automatic cleaning of whole-night sleep electroencephalogram using Artifact Subspace Reconstruction.

Proper citation: Dusk2Dawn (RRID:SCR_027417) Copy   

•   Source: SciCrunch Registry