Two step transfer entropy – An estimator of delayed directional couplings between multivariate EEG time series

Quantifying delayed directional couplings between electroencephalographic (EEG) time series requires an efficient method of causal network inference. This is especially due to the limited knowledge about the underlying dynamics of the brain activity. Recent methods based on information theoretic measures such as Transfer Entropy (TE) made significant progress on this issue by providing a model-free framework for causality detection. However, TE estimation from observed data is not a trivial task, especially when the number of variables is large which is the case in a highly complex system like human brain.

Source: Computers in Biology and Medicine - October 12, 2016 Category: Bioinformatics Authors: Maryam Songhorzadeh, Karim Ansari-Asl, Alimorad Mahmoudi Source Type: research