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Arnaud Delorme edited this page Aug 28, 2023 · 28 revisions
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The Source Information Flow Toolbox tutorial (SIFT)

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).