## 4 Discussion  
While considering Sensitivity and Precision, it was shown that, among the ANNs, LSTM-NUE yielded superior results in terms of Sensitivity, resulting in statistically significant differences with the other ANNs except for Conv2D with TS = 2. In terms of Precision, however, no significant differences among the ANNs were found while using Ground Truth 1. TRGC outperformed all ANNs in terms of Sensitivity but, statistically, no differences in Precision were found given that the main effect of the connectivity method was only marginally significant. The lack of a statistically significant effect of connectivity method on Precision, as well as the lack of an effect of dipole condition, and the lack of an interaction effect on both Sensitivity as well as Precision are quite counterintuitive. Indeed, given (1) the patterns observed across both Ground Truths and (2) the results from [1], which convincingly showed effects of different dipole conditions on connectivity patterns as well as interaction effects of connectivity method and dipole condition, one could at least expect an effect of dipole condition. For instance, in [1], it was shown that with an SNR of 0.9 and in a Far-Superficial dipole condition, false positives (as related to Precision) were rather rare, while for other dipole conditions, the percentage of false positives increases (hence decreasing Precision). A related (solely qualitative) observation is the variability in the results of the ANNs (as became obvious through the standard deviations from the mean as depicted in Figures 7 and 8) versus the stability of results produced by TRGC. In particular, ANNS seems to exhibit an increased variability in performance in the Far-Deep Condition (in contrast to the Far-Superficial condition), while almost no such variability is observed for TRGC. A possible culprit could be the initial randomization of the weights in ANNs, but how this instability could differ between architectures or between dipole conditions is unclear and deserves attention in future studies. One of the most important observations of Ground Truth 1 is the relatively poor Precision score of TRGC in the Far-Superficial condition, albeit that a difference with the Far-Deep dipole condition could not be statistically confirmed. More data may be needed to confirm the observed trends. The above-mentioned contrasting results are further discussed below, together with possible explanations with regard to the used connectivity methods. Using Ground Truth 2, no differences in Sensitivity between TRGC and LSTM-NUE were found given that both methods returned almost always a Sensitivity of one, while Precision was significantly higher for TRGC than for LSTM-NUE. The other ANNs did not detect any connection. The good performance of TRGC regarding Precision is not surprising. In [1], it was already shown that TRGC outperformed Multivariate Granger Causality (MVGC), especially when it comes to false positives (as reflected in a lower False Positive Rate), which is logical given that the introduction of time-reversal could indeed allow for a better distinction between correlated time series (due to linear mixtures of EEG signals) and true temporal precedence of one time series with regard to another. Although the idea of TRGC is relatively new (as it was first proposed in 2013, by [8]) in comparison to, for instance, bivariate GC and MVGC, due to its appealing theoretical properties as well as its further validation by [7], it was quickly picked up in the field, given its relevance for, among others, EEG source connectivity. Recent developments include, for instance, variations in TRGC that allow for other than normal distributions [32]. In summary, it became clear that, among the ANNs, LSTM-NUE obtained better Sensitivity scores and (although only statistically confirmed using Ground Truth 2) better Precision scores.