**5. Conclusions**

A novel valence recognition system has been presented and applied to EEG signals, which were recorded from volunteers subjected to emotional states elicited by pictures drawn from IAPS repository. A cohort of 26 female participants has been investigated. The recognition system encompasses a feature extraction stage and a classification module including feature elimination. The complete system focused on both an intersubject and an intrasubject situa‐ tion. Both studies show a similar performance with regard to the classification accuracy. The recursive feature elimination (selection) was designed based on a random forest classifier or support vector machine and increased the initial classification accuracy in a range from 20% to 45%. The importance measures from both algorithms point to frontal areas although no consistent set of features and related latencies could be identified.

This fact points toward a large biological variability of the set of relevant features corresponding to the valence of the emotional states involved. In any case, the classification accuracy achieved compares well with or is even superior to competing systems reported in the literature.

Comparing both classifiers, the SVM achieves better classification accuracy, yielding up to 100% accuracy rate for several subjects by using the selected features for intrasubject clas‐ sification task, outperforming random forest that reached an 81% maximum peak accuracy. Furthermore, the presented wrapper methods are a good option to greatly reduce the dimen‐ sion of the input space and deserve to be considered as an alternative for discriminating rel‐ evant scalp regions, frequency bands and time intervals from EEG recordings.
