Research of electroencephalography representational emotion recognition based on deep belief networks.
10.7507/1001-5515.201706035
- Author:
Hao YANG
1
;
Junran ZHANG
2
;
Xiaomei JIANG
1
;
Fei LIU
1
Author Information
1. School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, P.R.China.
2. School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, P.R.China.zhangjunran@126.com.
- Publication Type:Journal Article
- Keywords:
deep belief network;
deep learning;
electroencephalogram;
emotional recognition
- From:
Journal of Biomedical Engineering
2018;35(2):182-190
- CountryChina
- Language:Chinese
-
Abstract:
In recent years, with the rapid development of machine learning techniques,the deep learning algorithm has been widely used in one-dimensional physiological signal processing. In this paper we used electroencephalography (EEG) signals based on deep belief network (DBN) model in open source frameworks of deep learning to identify emotional state (positive, negative and neutrals), then the results of DBN were compared with support vector machine (SVM). The EEG signals were collected from the subjects who were under different emotional stimuli, and DBN and SVM were adopted to identify the EEG signals with changes of different characteristics and different frequency bands. We found that the average accuracy of differential entropy (DE) feature by DBN is 89.12%±6.54%, which has a better performance than previous research based on the same data set. At the same time, the classification effects of DBN are better than the results from traditional SVM (the average classification accuracy of 84.2%±9.24%) and its accuracy and stability have a better trend. In three experiments with different time points, single subject can achieve the consistent results of classification by using DBN (the mean standard deviation is1.44%), and the experimental results show that the system has steady performance and good repeatability. According to our research, the characteristic of DE has a better classification result than other characteristics. Furthermore, the Beta band and the Gamma band in the emotional recognition model have higher classification accuracy. To sum up, the performances of classifiers have a promotion by using the deep learning algorithm, which has a reference for establishing a more accurate system of emotional recognition. Meanwhile, we can trace through the results of recognition to find out the brain regions and frequency band that are related to the emotions, which can help us to understand the emotional mechanism better. This study has a high academic value and practical significance, so further investigation still needs to be done.
