Emotion Recognition With Knowledge Graph Based on Electrodermal Activity

Abstract

Electrodermal activity (EDA) sensor is emerging non-invasive equipment in affect detection research, which is 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 (SF) vectors for emotional state classification. We finally utilize deep neural networks to optimize our approach. Results obtained indicated that the correct combination of Gender-Age Relation Graph (GARG) and SF vectors improve the performance of the valence-arousal emotion recognition system by 4 and 5% on PAFEW and 3 and 2% on DEAP datasets.

EDA-Graph: Graph Signal Processing of Electrodermal Activity for Emotional States Detection

“…Furthermore, we demonstrated the usefulness of EDA-graph for detecting emotional states, by obtaining sensitive features from the EDAgraphs that allowed accurate differentiation between emotional states. The graph features showed superior performance compared to traditional EDA features (see asterisk tables in Figure 4 and Figure 5) [96]. The EDA-graph representation captured nonlinearities while discarding uninformative flat The multi-scale emotion-related information within the EDA-graph feature space demonstrates the advantages of graph representations for uncovering complex physiological state relationships relative to conventional signal analysis alone.…”