EDA-graph: Graph Signal Processing of Electrodermal Activity for
Emotional States Detection
Abstract
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
(NoC), diameter, and graph radius, and compared those features with
features obtained using traditional EDA processing techniques. EDA-graph
features encompassing TLC, THC, NoC, 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.