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The Source Information Flow Toolbox tutorial (SIFT)

Developed and Maintained by: Tim Mullen and Arnaud Delorme (SCCN, INC, UCSD) 2009- </tr> </tbody> </table> 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).


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