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Emotion Recognition Using WT-SVM in Human-Computer Interaction

Zequn Wang, Rui Jiao, Huiping Jiang*

Minzu University of China, Beijing, 100081, China

* Corresponding Author: Huiping Jiang. Email:

Journal of New Media 2020, 2(3), 121-130.


With the continuous development of the computer, people's requirements for computers are also getting more and more, so the brain-computer interface system (BCI) has become an essential part of computer research. Emotion recognition is an important task for the computer to understand social status in BCI. Affective computing (AC) aims to develop the model of emotions and advance the affective intelligence of computers. There are various emotion recognition approaches. The method based on electroencephalogram (EEG) is more reliable because it is higher in accuracy and more objective in evaluation than other external appearance clues such as emotion expression and gesture. In this paper, we use the wavelet transform (WT) to extract three kinds of EEG features in time, and frequency domain, which are sub-band energy, energy ratio and root mean square of wavelet coefficients. They reflect the emotion related to EEG activities well. The average classification accuracy of support vector machine (SVM) can reach 82.87%, which indicates that these three features are very effective in emotion recognition. On the other hand, compared with international affective picture system (IAPs), EEG data collected by Chinese affective picture system (CAPs) stimulation has a higher emotion recognition rate, indicating that there are cultural background differences in emotions.


Cite This Article

Z. Wang, R. Jiao and H. Jiang, "Emotion recognition using wt-svm in human-computer interaction," Journal of New Media, vol. 2, no.3, pp. 121–130, 2020.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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