@Article{cmc.2020.011793,
AUTHOR = {Huiping Jiang, Zequn Wang, Rui Jiao, Shan Jiang},
TITLE = {Picture-Induced EEG Signal Classification Based on CVC  Emotion Recognition System},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {2},
PAGES = {1453--1465},
URL = {http://www.techscience.com/cmc/v65n2/39887},
ISSN = {1546-2226},
ABSTRACT = {Emotion recognition systems are helpful in human–machine interactions and 
Intelligence Medical applications. Electroencephalogram (EEG) is closely related to the 
central nervous system activity of the brain. Compared with other signals, EEG is more 
closely associated with the emotional activity. It is essential to study emotion recognition 
based on EEG information. In the research of emotion recognition based on EEG, it is a 
common problem that the results of individual emotion classification vary greatly under 
the same scheme of emotion recognition, which affects the engineering application of 
emotion recognition. In order to improve the overall emotion recognition rate of the 
emotion classification system, we propose the CSP_VAR_CNN (CVC) emotion 
recognition system, which is based on the convolutional neural network (CNN) algorithm 
to classify emotions of EEG signals. Firstly, the emotion recognition system using 
common spatial patterns (CSP) to reduce the EEG data, then the standardized variance 
(VAR) is selected as the parameter to form the emotion feature vectors. Lastly, a 5-layer 
CNN model is built to classify the EEG signal. The classification results show that this 
emotion recognition system can better the overall emotion recognition rate: the variance 
has been reduced to 0.0067, which is a decrease of 64% compared to that of the 
CSP_VAR_SVM (CVS) system. On the other hand, the average accuracy reaches 
69.84%, which is 0.79% higher than that of the CVS system. It shows that the overall 
emotion recognition rate of the proposed emotion recognition system is more stable, and 
its emotion recognition rate is higher.},
DOI = {10.32604/cmc.2020.011793}
}