## 4 Discussion
An LSTM may also be better in looking through (uncorrelated) noise components because it remembers formerly seen time samples better and, subsequently, should be better in detecting (even weak) patterns over time, also when occluded by noise. This, in turn, may make it easier to deal with more challenging dipole locations or with heavier data transformations. However, this same property could also make an LSTM more sensitive to correlated noise from source mixing. TCDF, on the other hand, has the advantage of a very low Time Complexity, at least partly due to its sparsity in interconnection weights (given its depthwise-separable architecture), but it seems less able to distinguish correlation from causation. This may be due to the lack of feedback loops, an \"active\" memory feature that makes it difficult to distinguish true patterns from noise over longer time intervals. In this study, TCDF was tuned as such that not too many false positives were detected (given its problem of distinguishing correlation from causation), and this more \"conservative\" configuration may have led to its low Sensitivity. Overall, we can conclude that, among the ANN models, LSTM-NUE performed best in terms of Sensitivity and Precision regardless of which ground truth was used even though no shuffling or time-reversal was used for connectivity assessment. The contrasting results of TRGC in terms of Precision between dipole conditions in Ground Truths 1 and 2 are puzzling and clearly show an \"oversensitivity\" of TRGC under certain circumstances. Still, TRGC and LSTM-NUE yielded acceptable-to-good results, albeit both suffer from over-detection. An interesting new finding is the fact that an LSTM is, to some extent, able to provide an answer to the question of whether connectivity between sources is present or absent, at least for source-reconstructed, simulated EEG data. The fact that too many faulty connections were detected (especially in Ground Truth 2) calls for improvements. One possibility is to use LSTM-NUE as part of a masking approach, on top of which another learner is stacked. This masking approach has already led to many advantages in source localization [25], and it may also facilitate connectivity detection with ANNs, especially when overly sensitive to it. In this sense, other ANNs, even with a lower Time Complexity than that of LSTM-NUE, could possibly also be considered as potentially directed connectivity estimators. An obvious future step is testing whether ANNs can also be applied to real EEG data, albeit that several possible caveats should be taken into account. First and foremost, as shown by [1], under low noise conditions, dipole conditions may matter less, but differences between dipole conditions could become more obvious (i.e., more disturbing) under higher noise levels. Even long-established connectivity methods suffer from this. Since controlling noise levels is hard, reasonably one could opt for EEG-data for which (1) the contributing brain areas are rather superficially located, (2) the connectivity patterns are relatively well known and preferably supported by both high-density EEG and fMRI-data so that a performance evaluation becomes feasible since no ground truth is available for real EEG-data. Testing ANNs and contrasting them with TRGC/other established methods using vision-related or motor-related EEG-datasets makes thus more sense than testing them with data with relatively unknown connectivity patterns. Regions of Interest (ROIs) can be defined based upon previous knowledge about involved brain areas. As for source localization, a reasonable choice is eLORETA. Data-driven approaches (as opposed to ROI-selection), e.g., data-driven clustering [33], seem only reasonable in a later stage when the value of the used ANN is proven on real EEG data.