*4.2. EEG Data Results*

Table 1 presents the average accuracies achieved by the proposed classification systems for both the MI and WM databases, for each effective connectivity method studied. For the MI database, in the training-validation stage, the classifier based on TE*<sup>θ</sup> κα* features exhibited the highest average performance, closely followed by the one based on GC*θ*. In the testing stage, we observe the same overall accuracy ranking, although a smaller drop in the classification accuracy occurs for TE*<sup>θ</sup> κα* than for GC*θ*, which points to a better generalization capacity by the system trained using features extracted through phase TE. For the WM database, the classifier trained from TE*<sup>θ</sup> κα* features also displays the highest average accuracy. However, in this case, there is a large gap in performance between the TE*<sup>θ</sup> κα*-based classification system and the closest results from an alternative approach. Furthermore, the results in Table 1 show a consistent improvement in performance between the classifiers that use real-valued TE estimates and those that are trained from phase TE values. They also show relatively low accuracies for the classifiers trained using PSI features. We believe the latter can be explained by two factors. First, by definition, the PSI is unable to explicitly detect bidirectional interactions. It measures connectivity in terms of lead/lag relations, which leads to ambiguity regarding the meaning of PSI values close to zero, since they can be the result of either the lack of interaction or evenly balanced bidirectional connections. If the relevant information to discriminate among the conditions of a cognitive paradigm is related to the bidirectionality of interactions, such as those present in WM [50,51], then the PSI might not be an adequate characterization strategy. Secondly, the PSI, like GC, is a linear measure; its performance degrades for strongly nonlinear phase relationships.

In the sections below, we detail and further discuss the results obtained for each database.


**Table 1.** MI and WM classification results in terms of the classification accuracy for all the effective connectivity measures considered.
