Approach on affective valence detection from EEG signals based on global field power measure and SVM-RFE algorithm.

EEG signals have attracted the interest of scientific community for understanding how brain processes emotions. In order to extract objective conclusions, automatized methods that are able to reinforce the subjective visual explorations of the signals are desirable. In this work, a feature extraction + wrapped classification scheme is proposed for analysing how brain reacts to visual high/low valence stimuli and how the linked brain processes change when a novel or familiar stimulus is presented. For such purpose, experiments were carried out using the international affective picture system (IAPS) images. Global field power (GFP) from the recorded EEG signals is computed, and a support vector machine-recursive feature elimination (SVM-RFE) method is applied to the input signals. The combination of these techniques yielded up to 100% peak accuracy in both classification tasks, outperforming traditional statistical methods for group comparisons such as t-test.