Open Access
ARTICLE
Picture-Induced EEG Signal Classification Based on CVC Emotion Recognition System
Huiping Jiang1, *, Zequn Wang1, Rui Jiao1, Shan Jiang2
1 Brain Cognitive Computing Lab, School of Information Engineering, Minzu University of China, Beijing, 100081, China.
2 Information School, The University of Sheffield, Sheffield, UK.
* Corresponding Author: Huiping Jiang. Email: .
Computers, Materials & Continua 2020, 65(2), 1453-1465. https://doi.org/10.32604/cmc.2020.011793
Received 29 May 2020; Accepted 14 June 2020; Issue published 20 August 2020
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.
Keywords
Cite This Article
H. Jiang, Z. Wang, R. Jiao and S. Jiang, "Picture-induced eeg signal classification based on cvc emotion recognition system,"
Computers, Materials & Continua, vol. 65, no.2, pp. 1453–1465, 2020. https://doi.org/10.32604/cmc.2020.011793
Citations