A three-stage decision framework for multi-subject emotion recognition using physiological signals

This paper investigates the potential of physiological signals as reliable channels for multi-subject emotion recognition. A three-stage decision framework is proposed for recognizing four emotions of multiple subjects. The decision framework consists of three stages: (1) in the initial stage, ident...

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Bibliographic Details
Published in2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) pp. 470 - 474
Main Authors Jing Chen, Bin Hu, Yue Wang, Yongqiang Dai, Yuan Yao, Shengjie Zhao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2016
Subjects
affective computing
Brain models
Emotion recognition
Feature extraction
multimodal physiological signals
Physiology
Probability
subject-independent
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Summary:This paper investigates the potential of physiological signals as reliable channels for multi-subject emotion recognition. A three-stage decision framework is proposed for recognizing four emotions of multiple subjects. The decision framework consists of three stages: (1) in the initial stage, identifying a subject group that a test subject can be mapped to; (2) in the second stage, identifying an emotion pool that an instance of the test subject can be assigned to; and (3) in the final stage, generating the predicted emotion from the given emotion pool for the test instance. In comparison with a series of alternative methods, the high accuracy of 70.04% achieved by our proposed method clearly demonstrates the potential of the three-stage decision method in multi-subject emotion recognition.
DOI:10.1109/BIBM.2016.7822562