Framework of Information Theory for Electrophysiological data and Statistics
FRITES = Framework for Information Theoretical analysis of Electrophysiological data and Statistics
Frites is a python package for analyzing neurophysiological brain data (i.e M/EEG, sEEG / iEEG / ECoG). The package is entirely based on information theoretic measures (such as mutual information (MI)) in order to perform analysis such as :
- “Correlation like” (I(c; c) = MI between two continuous variables)
- “Machine-learning like” (I(c; d) = MI between a continuous and a discrete variable)
- “Partial correlation like” (I(c; c | d) = MI between two continuous variables and removing the influence of a discrete one)
- Information-transfer about a specific feature
For a comprehensive (and extensive) review, see the paper of Robin AA Ince A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula.
Frites also comes with embedded statistics which support fixed and random-effect analysis in combination with inferences either at the single time-point level or at the temporal cluster level.
Take a look at the online documentation and examples to start analyzing your data with Frites : https://brainets.github.io/frites/
The main dependencies of Frites are :
In addition to the main dependencies, here’s the list of additional packages that you might need :
Frites can be installed (and/or updated) via pip with the following command :
pip install -U frites
For developers, you can install frites in develop mode with the following commands :
git clone https://github.com/brainets/frites.git cd frites python setup.py develop
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