sccn/SIFT: SIFT is an EEGLAB-compatible toolbox for 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. · GitHub

Developed by: Tim Mullen 2009- Maintained: Tim Mullen and Arnaud Delorme

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).

• Arnaud Delorme was instrumental in the development of the SIFT framework and integration into EEGLAB as well as contributing initial BrainMovie3D code.
• Christian Kothe contributed the arg() framework for function I/O and auto-GUI generation
• Wes Thompson consulted on statistics and methods for bayesian smoothing and multi-subject analysis
• Alejandro Ojeda contributed routines for fast ridge regression

SIFT makes use of routines from (or is inspired by) the following open-source packages:

• ARFIT (Schneider et al)
• TSA/Biosig (Schlögl et al)
• Chronux (Mitra et al)
• DAL/SCSA (Tomioka / Haufe et al)
• BCILAB (Kothe et al)

See the SIFT wiki or use the submenus if you are looking at this page on the EEGLAB website.

If you find this toolbox useful for your research, PLEASE include the following citations with any publications and/or presentations which make use of SIFT:

1. Mullen, T. R. (2014). The dynamic brain: Modeling neural dynamics and interactions from human electrophysiological recordings (Order No. 3639187). Available from Dissertations & Theses @ University of California; ProQuest Dissertations & Theses A&I. (1619637939)
2. Delorme, A., Mullen, T., Kothe C., Akalin Acar, Z., Bigdely Shamlo, N., Vankov, A., Makeig, S. (2011) "EEGLAB, SIFT, NFT, BCILAB, and ERICA: New tools for advanced EEG/MEG processing." Computational Intelligence and Neuroscience vol. 2011, Article ID 130714, 12 pages.

SIFT is licensed under the GPL-2, see LICENSE.txt ANY USE OF SIFT IMPLIES THAT YOU HAVE READ AND AGREE WITH THE TERMS AND CONDITIONS OF THE SIFT LICENSE AS STATED BELOW:

SIFT is designed and distributed for research purposes only. SIFT should not be used for medical purposes. The authors accept no responsibility for its use in this manner.

v1.6 - fix conflict with BrainMovie plugin. Fix minor GUI issues.