*2.3. Phase-Based Effective Connectivity Estimation Approaches Considered in This Study* 2.3.1. Phase Transfer Entropy

We obtain phase TE values through three different estimators that allow computing TE from individual signal pairs. First, the proposed kernel-based Renyi's phase TE estimator (TE*<sup>θ</sup> κα*), defined in Equation (9). Second, the Kraskov-Stögbauer-Grassberger TE estimator (TE*<sup>θ</sup> KSG*), a method that relies on a local approximation of the probability distributions needed to estimate the entropies in TE from the distances of every data point to its neighbors [33,34]. Thirdly, an approach termed symbolic TE (TE*<sup>θ</sup> Sym*) that relies on a symbolization scheme based on ordinal patterns. The symbolization scheme allows estimating the probabilities involved in the computation of TE directly from the symbols' relative frequencies [35].

In all cases, *θ<sup>x</sup>* and *θ<sup>y</sup>* are obtained by convolving the real-valued time series with a Morlet wavelet, defined as

$$h(t,f) = \exp(-t^2/2\mathbb{Z}\_{\sharp}^2)\exp(i2\pi ft),\tag{10}$$

where *f* stands for the filter frequency, *ξ<sup>t</sup>* = *m*/2*π f* is the time domain standard deviation of the wavelet, and *m* defines the compromise between time and frequency resolution [8].
