Conditional generative adversarial network for EEG-based emotion fine-grained estimation and visualization

Abstract

In the field of affective computing (AC), coarse-grained AC has been developed and widely applied in many fields. Electroencephalogram (EEG) signals contain abundant emotional information. However, it is difficult to develop fine-grained AC due to the lack of fine-grained labeling data and suitable visualization methods for EEG data with fine labels. To achieve a fine mapping of EEG data directly to facial images, we propose a conditional generative adversarial network (cGAN) to establish the relationship between EEG data associated with emotions, a coarse label, and a facial expression image in this study. In addition, a corresponding training strategy is also proposed to realize the fine-grained estimation and visualization of EEG-based emotion. The experiments prove the reasonableness of the proposed method for the generation of fine-grained facial expressions. The image entropy of the generated image indicates that the proposed method can provide a satisfactory visualization of fine-grained facial expressions.