Introduction

The HyperIT Class is a framework that calculates Mutual Information (MI), Transfer Entropy (TE), and Integrated Information Decomposition ($Phitext{ID}$) for both hyperscanning and intra-brain analyses.

Handling continuous time-series data (epoched or otherwise), HyperIT computes these information-theoretic measures at different frequency resolutions and different spatial scales of organisation (micro, meso, and macro) — compatible with EEG, MEG, fMRI, and fNIRS data. Offers parameter customisation and estimator selection (Histogram/Binning, KSG, Box Kernel, Gaussian, and Symbolic) via JIDT. Most estimators are equipped with statistical significance testing based on permutation/bootstrapping approaches, too. Visualisations of MI/TE matrices and information atoms/lattices also provided.

In all, HyperIT is designed to allow researchers to analyse various complex systems at different scales of organisation deploying information-theoretic measures, particularly focusing on neural time-series data in the context of hyperscanning.