Frites is a Python toolbox for assessing information-based measures on human and animal neurophysiological data (M/EEG, Intracranial). The toolbox also includes directed and undirected connectivity metrics such as group-level statistics on measures of information (information-theory, machine-learning and measures of distance).
What can you do with Frites?#
Frites can extract task-related cognitive brain networks, that is brain regions and connectivity between brain regions that are modulated according to the task (overview)
Frites can be used to assess Dynamic Functional Connectivity using mutual information and directional measures using Granger causality (examples)
Frites can perform statistical inference on measures of information, such as measures from information-theory, machine-learning or measures of distances, using permutation-based tests (overview and examples) and controlling for multiple comparisons (overview)
Measures of connectivity
Estimate whole-brain pairwise undirected and directed connectivity.