“…Fortunately, certain entropy estimators are minimally affected by the nonstationarity of a signal. One such estimator is the nonparametric k-th nearest neighbor (KNN) entropy estimator [ 26 , 27 , 28 , 29 , 30 ], which was used here to compute the entropies composing I(X) and C I (X) . Here, the entropy of a data space is estimated by approximating the multivariate probability density function, f X (X) , at each point, x i , in the space in terms of the ratio of the fraction of other points in a data point’s neighborhood to the volume of its neighborhood [ 29 ]: where N p is the number of data points in the space, Vol i is the volume of the neighborhood, and k(x i ) is the number of points in the neighborhood of x i other than x i .…”