Clone this wiki locally
Developed and Maintained by: Tim Mullen and Arnaud Delorme (SCCN, INC, UCSD) 2009-
SIFT is an EEGLAB-compatible toolbox for the analysis and visualization of multivariate causality and information flow between sources of electrophysiological (EEG/ECoG/MEG) activity. It consists of a suite of command-line functions with an integrated Graphical User Interface for easy access to multiple features. There are currently six modules: data preprocessing, model fitting and connectivity estimation, statistical analysis, visualization, group analysis, and neuronal data simulation.
Methods currently implemented include:
- Preprocessing routines
- Time-varying (adaptive) multivariate autoregessive modeling
- granger causality
- directed transfer function (DTF, dDTF)
- partial directed coherence (PDC, GPDC, PDCF, RPDC)
- multiple and partial coherence
- event-related spectral perturbation (ERSP)
- and many other measures...
- Bootstrap/resampling and analytical statistics
- event-related (difference from baseline))
- between-condition (test for condition A = condition B)
- A suite of programs for interactive visualization of information flow dynamics across time and frequency (with optional 3D visualization in MRI-coregistered source-space).