Automatic ECG-Based Emotion Recognition in Music Listening

• Published in: IEEE transactions on affective computing Vol. 11; no. 1; pp. 85 - 99
• Main Authors: Hsu, Yu-Liang, Wang, Jeen-Shing, Chiang, Wei-Chun, Hung, Chien-Han
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Arousal; Classification; Discriminant analysis; Electrocardiogram; Electrocardiography; Emotion recognition; Emotions; Feature extraction; machine learning; Multiple signal classification; Music; Nonlinear analysis; Physiology; Support vector machines
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2017.2781732

This paper presents an automatic ECG-based emotion recognition algorithm for human emotion recognition. First, we adopt a musical induction method to induce participants' real emotional states and collect their ECG signals without any deliberate laboratory setting. Afterward, we develop an automatic ECG-based emotion recognition algorithm to recognize human emotions elicited by listening to music. Physiological ECG features extracted from the time-, and frequency-domain, and nonlinear analyses of ECG signals are used to find emotion-relevant features and to correlate them with emotional states. Subsequently, we develop a sequential forward floating selection-kernel-based class separability-based (SFFS-KBCS-based) feature selection algorithm and utilize the generalized discriminant analysis (GDA) to effectively select significant ECG features associated with emotions and to reduce the dimensions of the selected features, respectively. Positive/negative valence, high/low arousal, and four types of emotions (joy, tension, sadness, and peacefulness) are recognized using least squares support vector machine (LS-SVM) recognizers. The results show that the correct classification rates for positive/negative valence, high/low arousal, and four types of emotion classification tasks are 82.78, 72.91, and 61.52 percent, respectively.