Emotion Recognition With Knowledge Graph Based on Electrodermal Activity - Perry Fordson, Hayford; Xing, Xiaofen; Guo, Kailing; Xu, Xiangmin (Frontiers in Neuroscience, 2022)
Electrodermal Activity (EDA) sensor is an emerging non-invasive equipment in affect detection research used to measure electrical activities of the skin. Knowledge graphs are an effective way to learn representation from data. However, few studies analyzed the effect of knowledge-related graph features with physiological signals when subjects are in non-similar mental states. In this paper, we propose a model using deep learning techniques to classify the emotional responses of individuals acquired from physiological datasets. We aim to improve the execution of emotion recognition based on EDA signals. The proposed framework is based on observed gender and age information as embedding feature vectors. We also extract time and frequency EDA features in line with cognitive studies. We then introduce a sophisticated weighted feature fusion method that combines knowledge embedding feature vectors and statistical feature vectors for emotional state classification. We finally utilize deep neural networks to optimize our approach. Results obtained indicated that correct combination of Gender-Age Relation Graph (GARG) and Statistical Feature (SF) vectors improve the performance of valence-arousal emotion recognition system by 4% and 5% on PAFEW and 3% and 2% on DEAP datasets.