**Author details**

**Figures 8** and **9** show the relevance of the features chosen by the two methods in topographi‐ cal maps. As can be seen from **Figure 8**, on average, both algorithms allocate the most relevant features in the frontal region in agreement with intersubject applications. Similarly, both also identify relevant features mostly in the beta bands. According to **Figure 9**, both algorithms allocate important features showing up with short latencies in the frontal areas of the brain. Concerning *medium* and *long* latencies both algorithms again identify important features in

Although intersubject and intrasubject methodologies show a similar performance, they have different application scenarios. The intersubject classification is mostly suitable for offline appli‐ cations as well as for brain studies in order to complement the statistical methods. For instance, in Ref. [49], an SVM‐RFE scheme was exclusively applied to identify scalp spectral dynamics linked with the affective valence processing and to compare with standard statistical results (*t*‐test). In that work, a different technique for feature extraction was developed, whose goals consisted of creating a particular volume of features by means of a wavelet filtering. In this way, a high‐dimensional data set was represented by means of three dimensions: frequency (resolution: 1 Hz), time (resolution: 1 ms) and topographical location (21 EEG channels).

Due to the biological variability observed, intrasubject studies cannot generalize easily across a cohort of subjects. Thus, intrasubject approaches might be interesting for personalized stud‐ ies where subjects need to be followed up for a couple of sessions, such as in a rehabilita‐ tion therapy, or for neurofeedback‐based applications. An example of an intrasubject study is reported in Ref. [50], where the neuroticism trait is analyzed using EEG to check the influ‐ ence of individual differences in the emotional processing and the susceptibility of each brain region. In that work SVM was used as well, although from a different standpoint, since it was

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

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.

performed in subject identification tasks from single trials.

40 Emotion and Attention Recognition Based on Biological Signals and Images

consistent set of features and related latencies could be identified.

**5. Conclusions**

frontal areas though their importance is more pronounced with the random forest.

Antonio R. Hidalgo‐Muñoz<sup>1</sup> \*, Míriam M. López<sup>1</sup> , Isabel M. Santos2 , Manuel Vázquez‐Marrufo<sup>3</sup> , Elmar W. Lang<sup>4</sup> and Ana M. Tomé<sup>1</sup>

