Emotional States Detection Using Electrodermal Activity and Graph Signal Processing

This study introduces a novel Graph Signal Processing (GSP) method to analyze Electrodermal Activity (EDA) signals for emotional state detection. EDA, influenced by the sympathetic nervous system, is a sensitive indicator of emotional states but is characterized by complex nonstationary and nonlinear properties. Our novel approach transforms EDA signals into graphical networks, termed EDA-graphs, using GSP to unravel intricate relationships in time-series data. We used the CASE dataset and created EDA-graphs by quantizing the signals and grouping values based on Euclidean distances between nearest neighbors. From the EDA-graphs we computed and analyzed graph-based features including Total Load Centrality (TLC), Total Harmonic Centrality (THC) and Number of Cliques (NoC). These features were compared with those derived from traditional EDA processing techniques for emotional state detection. The results showed that EDA-graph features (TLC, THC and NoC), exhibited more significant differences across the five emotional states considered in this study (Neutral, Amused, Bored, Relaxed, and Scared) compared to traditional features of EDA, demonstrating the potential of our GSP approach in enhancing emotional state detection using EDA.