Prediction of Two-dimensional Impressions of Images of Facial Emotions using features of EEGs

Abstract

The viewing of categories of facial emotions is predicted using features of viewer’s scalp potentials, such as event-related potentials (ERPs) measured during the viewing of pictures of facial emotions. All visual stimuli were rated using two-dimensional emotional scales, and the responses for each viewer were converted into sensitivities using item response theory (IRT). This sensitivity to facial emotions can be predicted using discrimination analysis and the extracted features of ERPs recorded during the viewing of the images. The categories of facial emotions viewed were estimated to a certain level of significance using regression analysis, and the sensitivities predicted for the emotional scales were calculated accurately, as performance depended on the reactions to the images of the emotions. The results showed that categories of facial emotions viewed can be predicted to a level of significance using features of scalp potentials.