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https://github.com/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.
https://github.com/sccn/sift
causality connectivity eeg eeglab meg neural
Last synced: 3 months ago
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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.
- Host: GitHub
- URL: https://github.com/sccn/sift
- Owner: sccn
- License: other
- Created: 2017-11-05T06:38:48.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2024-08-02T19:18:30.000Z (6 months ago)
- Last Synced: 2024-08-02T21:52:31.537Z (6 months ago)
- Topics: causality, connectivity, eeg, eeglab, meg, neural
- Language: MATLAB
- Homepage: https://sccn.ucsd.edu/wiki/SIFT
- Size: 198 MB
- Stars: 30
- Watchers: 7
- Forks: 5
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
## The Source Information Flow Toolbox
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).
## Acknowledgements
- 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](https://github.com/tapios/arfit) (Schneider et al)
- [TSA/Biosig](http://octave.sourceforge.net/tsa/) (Schlögl et al)
- [Chronux](https://chronux.org) (Mitra et al)
- [DAL/SCSA](https://ttic.uchicago.edu/~ryotat/softwares/dal/) (Tomioka / Haufe et al)
- [BCILAB](http://sccn.ucsd.edu/wiki/BCILAB) (Kothe et al)
## Documentation
See the [SIFT wiki](http://sccn.ucsd.edu/wiki/SIFT) or use the submenus if you are looking at this page on the EEGLAB website.
## Citation
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.
## License
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:
## ADDITIONAL NOTE
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.
## Verions
v1.6 - fix conflict with BrainMovie plugin. Fix minor GUI issues.