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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   

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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   

•   Source: SciCrunch Registry

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://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://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://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://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