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

The continuous detection of emotional states has many applications in mental health, marketing, human-computer interaction, and assistive robotics. Electrodermal activity (EDA), a signal modulated by sympathetic nervous system activity, provides continuous insight into emotional states. However, EDA possesses intricate nonstationary and nonlinear characteristics, making the extraction of emotion-relevant information challenging. We propose a novel graph signal processing (GSP) approach to model EDA signals as graphical networks, termed EDA-graph. The GSP leverages graph theory concepts to capture complex relationships in time-series data. To test the usefulness of EDA-graphs to detect emotions, we processed EDA recordings from the CASE emotion dataset using GSP by quantizing and linking values based on the Euclidean distance between the nearest neighbors. From these EDA-graphs, we computed the features of graph analysis, including total load centrality (TLC), total harmonic centrality (THC), number of cliques (GNC), diameter, and graph radius, and compared those features with features obtained using traditional EDA processing techniques. EDA-graph features encompassing TLC, THC, GNC, diameter, and radius demonstrated significant differences (p < 0.05) between five emotional states (Neutral, Amused, Bored, Relaxed, and Scared). Using machine learning models for classifying emotional states evaluated using leave-one-subject-out cross-validation, we achieved a five-class F1 score of up to 0.68.

I. Introduction

Understanding emotional states is a complex but crucial task, given their significant impact on various aspects of life and technology [1]. Emotional states, encompassing subjective experiences, cognitive processes, physiological responses, and behavioral expressions, play a pivotal role in fields such as mental healthcare, human-computer interaction, and assistive robotics [2], [3]. For instance, accurately identifying emotional states like anxiety and sadness is key in treating mood disorders, which affect a substantial global population [4], [5]. In the realm of human-computer interactions, recognizing and responding to emotional states such as confusion or frustration can greatly enhance user experience [6]. Similarly, assistive robots that can perceive and adapt to human emotional states promise more seamless human-robot collaboration [2], [6].