{"id":19252312,"url":"https://github.com/sccn/sift","last_synced_at":"2025-04-21T13:30:54.734Z","repository":{"id":50116324,"uuid":"109557547","full_name":"sccn/SIFT","owner":"sccn","description":"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. 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It consists of a suite of\ncommand-line functions with an integrated Graphical User Interface for\neasy access to multiple features. There are currently six modules: data\npreprocessing, model fitting and connectivity estimation, statistical\nanalysis, visualization, group analysis, and neuronal data simulation.\n\nMethods currently implemented include:\n\n-   Preprocessing routines\n-   Time-varying (adaptive) multivariate autoregessive modeling\n    -   Granger causality\n    -   directed transfer function (DTF, dDTF)\n    -   partial directed coherence (PDC, GPDC, PDCF, RPDC)\n    -   multiple and partial coherence\n    -   event-related spectral perturbation (ERSP)\n    -   and many other measures...\n-   Bootstrap/resampling and analytical statistics\n    -   event-related (difference from baseline))\n    -   between-condition (test for condition A = condition B)\n-   A suite of programs for interactive visualization of information\n    flow dynamics across time and frequency (with optional 3D\n    visualization in MRI-coregistered source-space).\n\n## Acknowledgements\n\n- Arnaud Delorme was instrumental in the development of the SIFT framework and integration into EEGLAB as well as contributing initial BrainMovie3D code.\n- Christian Kothe contributed the arg() framework for function I/O and auto-GUI generation\n- Wes Thompson consulted on statistics and methods for bayesian smoothing and multi-subject analysis\n- Alejandro Ojeda contributed routines for fast ridge regression\n\nSIFT makes use of routines from (or is inspired by) the following open-source packages:\n\n- [ARFIT](https://github.com/tapios/arfit) (Schneider et al)\n- [TSA/Biosig](http://octave.sourceforge.net/tsa/) (Schlögl et al)\n- [Chronux](https://chronux.org) (Mitra et al)\n- [DAL/SCSA](https://ttic.uchicago.edu/~ryotat/softwares/dal/) (Tomioka / Haufe et al)\n- [BCILAB](http://sccn.ucsd.edu/wiki/BCILAB) (Kothe et al)\n\n\n## Documentation\n\nSee the [SIFT wiki](http://sccn.ucsd.edu/wiki/SIFT) or use the submenus if you are looking at this page on the EEGLAB website.\n\n## Citation\n\nIf you find this toolbox useful for your research, PLEASE include the following citations with any publications and/or presentations which make use of SIFT:\n\n1. Mullen, T. R. (2014). The dynamic brain: Modeling neural dynamics and interactions from human electrophysiological recordings (Order No. 3639187). Available from Dissertations \u0026 Theses @ University of California; ProQuest Dissertations \u0026 Theses A\u0026I. (1619637939)\n2. 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.\n\n## License\n\nSIFT is licensed under the GPL-2, see LICENSE.txt\nANY USE OF SIFT IMPLIES THAT YOU HAVE READ AND AGREE WITH THE TERMS AND CONDITIONS OF THE SIFT LICENSE AS STATED BELOW:\n\n## ADDITIONAL NOTE\n\nSIFT 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.\n\n## Verions\n\nv1.6 - fix conflict with BrainMovie plugin. Fix minor GUI issues.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsccn%2Fsift","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsccn%2Fsift","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsccn%2Fsift/lists"}