Conclusions
The advanced clustering methodology we developed shows promising results in the context of broad and heterogeneous EEG signalsThe stimulation target areas studied were the lDLPFC and rIFG, paired or not with concurrent tasks N-Back and Flanker Tasks respectively. Effectiveness of the treatment was assessed by behavioral reaction times and accuracy, when performing experimental tasks after the tDCS treatment. The digital phenotypes obtained from pre-treatment EEG, as computed by the clustering procedure described herein, can then be used to predict the response of individuals to particular stimulation protocols. We have successfully developed a procedure for labelling clusters in terms of their behavioral response in the different tasks. Moreover, a rank-sum Wilcoxon test is successfully applied for validation and consistency of results.Interestingly, positive responder groups correspond to participants with a non-typical brain profile, presenting either a slowing of the EEG or a spread of increased alpha rhythm over frontal areas. This finding is extremely interesting from a neurophysiological point of view. However, we focus in this communication on the methodological novelty of our work. A larger cohort of participants would be needed for confirming the clinical utility of the proposed approach, which we aim to achieve in future communications.