Granger causality is defined as the log-ratio between the error variance of a reduced model, which predicts one time series based only on its own past values, and that of the full model, which in addition includes the past values of another time series. In other words, it can be considered as directional functional connectivity. Granger causality reflects the strength of effective connectivity (i.e., causal interactions, extract activity of one are of causal influences of one neural element over another) from one region to another by quantifying how much the signal in the seed region is able to predict the signal in the target region ( Geweke, 1982 Granger, 1969). Wing Ting To, in Progress in Brain Research, 2021 3.9 Granger causality
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