Open Access
ARTICLE
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. https://doi.org/10.32604/jnm.2020.010674
Received 18 March 2020; Accepted 07 August 2020; Issue published 04 September 2020
Abstract
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.
Keywords
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. https://doi.org/10.32604/jnm.2020.010674
Citations