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 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.

1 Introduction

With rapid advancements in technology, advanced and user-friendly human-computer interaction (HCI) systems should become capable of considering human affective states in interactions to promote mutual sympathy between humans and machines. In human communication, the expression and understanding of emotions can help to achieve mutual sympathy. To develop an emotion-based HCI, we need to equip machines with the abilities to understand and identify human emotions without any input devices to translate users’ intentions. However, emotion is a psychological and physiological expression and is associated with mood, temperament, personality, disposition, and motivation, which are produced by cognitive processes, subjective feelings, motivational tendencies, behavioral reactions, and physiological arousal. Therefore, developing an effective emotion recognition system for identifying various emotions is an interesting and challenging topic.