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  • Installation
  • API Reference
  • Examples

Site Navigation

  • Overview
  • Installation
  • API Reference
  • Examples

Section Navigation

  • Tutorials
    • Estimate the Dynamic Functional Connectivity
    • Statistical analysis of a stimulus-specific network
  • Multi-subjects dataset
    • Define an electrophysiological dataset using MNE-Python structures
    • Build an electrophysiological dataset
    • Define an electrophysiological dataset using Xarray
  • Group-level statistics on measures of information
    • MI between two continuous variables conditioned by a discret one
    • MI between a continuous and a discret variables
    • MI between two continuous variables
    • Compute MI across time and frequencies
    • Investigate relation of order
    • Compute a conjunction analysis on mutual-information
    • Mutual-information at the contact level
  • Information-based estimators
    • Trial-resampling: correcting for unbalanced designs
    • Defining a custom estimator
    • Estimator comparison
  • Connectivity and Information Transfer
    • FIT: Feature specific information transfer
    • PID: Decomposing the information carried by pairs of brain regions
    • Estimate dynamic functional connectivity
    • Estimate interaction information
    • Estimate comodulations between brain areas
    • Lag estimation between delayed times-series using the cross-correlation
    • Estimate the covariance-based Granger Causality
  • Autoregressive model
    • AR : pairwise illustration
    • AR : simulate common driving input
    • AR : conditional covariance based Granger Causality
  • Utility
    • Define temporal windows
  • Statistics
    • Compare within-subjects statistics when computing mutual information
    • Compare between-subjects statistics when computing mutual information
    • Estimate the empirical confidence interval
  • Simulations
    • Generate random electrophysiological data
    • Generate spatio-temporal ground-truths
  • Performance
    • Comparison between tensor and vector based computations
  • Xarray
    • Xarray : Saving the results
    • Xarray : Quick tour
  • Examples
  • Information-based estimators

Information-based estimators#

Set of examples illustrating how to use Frites’ information-based estimators.

Trial-resampling: correcting for unbalanced designs

Trial-resampling: correcting for unbalanced designs

Defining a custom estimator

Defining a custom estimator

Estimator comparison

Estimator comparison

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Trial-resampling: correcting for unbalanced designs
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