Nonlinear Signal Processing Methods for Automatic Emotion Recognition Using Electrodermal Activity

• Published in: IEEE sensors journal Vol. 24; no. 6; pp. 8079 - 8093
• Main Authors: Veeranki, Yedukondala Rao, Diaz, Luis Roberto Mercado, Swaminathan, Ramakrishnan, Posada-Quintero, Hugo F.
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.03.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Affective computing; Classification; Classifiers; Complexity; Complexity theory; Decision trees; electrodermal activity (EDA); emotion analysis; Emotion recognition; Emotional factors; Emotions; Feature extraction; Machine learning; nonlinear signal processing; Physiology; Sensors; Signal processing; Support vector machines; Time-frequency analysis
• DOI: 10.1109/JSEN.2024.3354553
• ISSN: 1530-437X

Detection of emotional states plays a prominent role in affective computing, decision-making, and healthcare. Physiological signals are an ideal target for continuous and objective assessment of emotional states. Electrodermal activity (EDA) is considered one of the most effective and widely used markers of sympathetic activation. However, emotional state assessment using EDA is challenging. We hypothesize that this is caused by the impossibility of current EDA analysis tools to capture the nonlinear variations of the signal. Selecting an appropriate nonlinear signal processing method will allow more effective emotion recognition using EDA signals. In this study, we have compared the performance of four nonlinear signal processing methods-improved symbolic aggregate approximation (isaxEDA), complexity analysis (comEDA), topological analysis (topEDA), and network theory-based analysis (netEDA)-for classifying emotional states using EDA signals. For this, EDA signals were obtained from the publicly available continuously annotated signals of emotion (CASE) dataset. Features are extracted from each of the nonlinear signal processing methods, encompassing aspects related to the dynamics, complexity, structure, and network properties of EDA. Subsequently, the extracted features from each method were employed as input for four machine learning classifiers: naïve Bayes, random forest, <inline-formula> <tex-math notation="LaTeX">{k} </tex-math></inline-formula>-nearest neighbors, and support vector machine, which were validated using leave-one-subject-out cross validation. The results demonstrate that isaxEDA achieved the highest performance, with an F1-score of 65% using an SVM classifier. The study supports the suitability of nonlinear EDA signal processing to improve emotion recognition, which could be used to detect mental health conditions such as anxiety and depression.