1 College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027
2 Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310027
3 Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal
Effectiveness Appraisal, Zhejiang University, Hangzhou, 310027
4 Department of Psychology and Behavioral Science, Zhejiang University, Hangzhou, 310027
Emotion is a series of reactions triggered by a specific object or situation that affects a person's
physiological state and can, therefore, be identified by physiological signals. This paper proposes an
emotion recognition model. Extracted the features of physiological signals such as photoplethysmography,
galvanic skin response, respiration amplitude, and skin temperature. The SVM-RFE-CBR(Recursive
Feature Elimination-Correlation Bias Reduction-Support Vector Machine) algorithm was performed to
select features and support vector machines for classification. Finally, the model was implemented on
the DEAP dataset for an emotion recognition experiment. In the rating scale of valence, arousal, and
dominance, the accuracy rates of 73.5%, 81.3%, and 76.1% were obtained respectively. The result shows
that emotional recognition can be effectively performed by combining a variety of physiological signals.
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