In this tutorial, we have introduced a new, open-source (Matlab-based) toolbox for electrophysiological information flow analysis, which functions as a plugin for the EEGLAB environment. We sought to outline the theoretical basis for vector autoregressive (VAR) model fitting of electrophysiological data, as well as some VAR-based measures for multivariate granger-causality and spectral analysis in the time and frequency domain. We then demonstrated the applicability of these approaches through simulations and, using the SIFT toolbox, the analysis of EEG data from an individual performing an error-inducing cognitive task.

Acknowledgements

I (Tim Mullen) would like to first express my deep appreciation to Arnaud Delorme and Christian Kothe who have helped in the development of this toolbox. Dr. Delorme is the principal developer of EEGLAB and has helped substantially with integrating the toolbox into the EEGLAB environment as well as with modifications of his brainmovie3d.m and dipoledensity.m functions on which the causal brainmovie and causal projection functions have been based. Mr. Kothe contributed to many conversations regarding the structure of the toolbox and contributed the function input/output specification and PropertyGrid code, which is used in some of the graphical user interfaces. Thanks also to Derrick Lock for significant help in preparing the online wiki version of this manual.

I would also like to thank Scott Makeig for his constant encouragement and intellectual contributions in developing this toolbox. Additionally, I’d like to thank my undergraduate/postgraduate advisor Dr. Robert Knight (UC Berkeley) who supported my development of the ECViz toolbox on which the concept of SIFT was based.

Thanks also goes to Virginia De Sa and to Doug Nitz for serving as my project committee members and for their constant patience throughout the development of this project.

Finally, a big thanks goes to Nima Bigdely Shamlo, Julie Onton, Thorsten Zander and others from SCCN and elsewhere who have contributed ideas, datasets, visualization code, and other useful items to this project.

The author (Tim Mullen) was generously supported by a San Diego Fellowship, a Glushko Fellowship (Dept. of Cognitive Science), and endowments from the Swartz Foundation (Old Field, NY).

Parametric model-fitting in SIFT makes use of modified routines from Alois Schloegl’s open-source TSA+NaN package and, if downloaded separately, Tapio Schneider and Arnold Neumaier’s ARfit package.

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