@Article{jnm.2020.010674,
AUTHOR = {Zequn Wang, Rui Jiao, Huiping Jiang},
TITLE = {Emotion Recognition Using WT-SVM in Human-Computer Interaction},
JOURNAL = {Journal of New Media},
VOLUME = {2},
YEAR = {2020},
NUMBER = {3},
PAGES = {121--130},
URL = {http://www.techscience.com/JNM/v2n3/40103},
ISSN = {2579-0129},
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.},
DOI = {10.32604/jnm.2020.010674}
}