Comparison of Electrodermal Activity Signal Decomposition Techniques for Emotion Recognition

• Published in: IEEE access Vol. 12; pp. 19952 - 19966
• Main Authors: Veeranki, Yedukondala Rao, Ganapathy, Nagarajan, Swaminathan, Ramakrishnan, Posada-Quintero, Hugo F.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Arousal; classification; Computational modeling; Convexity; Decomposition; Deconvolution; electrodermal activity; Emotion; Emotion recognition; Emotions; Feature extraction; Magnetohydrodynamics; phasic component; Physiology; Support vector machines; Sympathetic nervous system; tonic component
• DOI: 10.1109/ACCESS.2024.3361832
• ISSN: 2169-3536

Emotions play an essential role in human life as they are linked to well-being and markers of various diseases. Physiological signals can be used to assess emotions objectively and continuously. Electrodermal activity (EDA) is particularly interesting to assess emotions due to its relationship with the sympathetic nervous system. EDA signals are composed of tonic and phasic components that react differently to emotions, and various methods are available to obtain these components. However, the most accurate and effective method used for emotion analysis based on the phasic component of EDA has not been reported so far. This study presents the comparison of various EDA decomposition methods used for emotion detection based on levels of affective dimensions (arousal and valence) levels (low vs. high). In this study, EDA was decomposed using six methods, namely convex optimization-based EDA(cvxEDA), Time-Varying Sympathetic Activity (TVSymp), continuous decomposition analysis (CDA), dynamic causal modeling (DCM), BayesianEDA, and sparse deconvolution approach (Sparse). To test the most usable decomposition method for objective assessment of emotions, EDA signals from the database for emotion analysis using physiological signals (DEAP) were obtained. Statistical, morphological, Hjorth, and non-linear Entropy features were extracted from the phasic component obtained from each decomposition method and fed to the Random Forest and support vector machine classifiers for detection of arousal and valence affective dimension. TVSymp yielded the highest F1 score of 72.79% and 73.49% for classifying Arousal and Valence, respectively.