Automatic ECG-Based Emotion Recognition in Music Listening

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 auto...

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Bibliographic Details
Published in:IEEE transactions on affective computing 2020-01, Vol.11 (1), p.85-99
Main Authors: Hsu, Yu-Liang, Wang, Jeen-Shing, Chiang, Wei-Chun, Hung, Chien-Han
Format: Article
Language:English
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
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Summary: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.
ISSN:1949-3045
1949-3045
DOI:10.1109/TAFFC.2017.2781732