Swartz Center for Computational Neuroscience, Institute for Neural Computation GNU General Public License (GPL) Yes University of California, San Diego NITRC Source Information Flow Toolbox Yes The Source Information Flow Toolbox (SIFT) is an 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. 2016-11-17 Latest Build 2016-11-17 1.4.1 2013-11-10 SIFT_Beta 2011-12-16 SIFT_Alpha Source Information Flow Toolbox EEG/MEG/ECoG, Directed Transfer Analysis, Granger Causality, Partial Directed Coherence, GNU General Public License (GPL) http://www.nitrc.org/projects/sift/, http://sccn.ucsd.edu/wiki/SIFT