Open Access

ARTICLE

Picture-Induced EEG Signal Classification Based on CVC Emotion Recognition System

by Huiping Jiang, Zequn Wang, Rui Jiao, Shan Jiang

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.

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