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

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 ma...