Research on electroencephalogram emotion recognition based on the feature fusion algorithm of auto regressive model and wavelet packet entropy.
10.7507/1001-5515.201610047
- Author:
Xin LI
1
,
2
;
Xiaoqi SUN
1
,
3
;
Xin WANG
1
,
3
;
Chunyan SHI
1
,
3
;
Jiannan KANG
4
;
Yongjie HOU
5
Author Information
1. Institute of Biomedical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P.R.China
2. Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, Hebei 066004, P.R.China.yddylixin@ysu.edu.cn.
3. Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, Hebei 066004, P.R.China.
4. College of Electronic & Information Engineering, Heibei University, Baoding, Hebei 071002, P.R.China.
5. HRA Medical Systems Co., Ltd,, Qinhuangdao, Hebei 066004, P.R.China.
- Publication Type:Journal Article
- Keywords:
auto-regressive;
electroencephalogram;
emotion recognition;
kernel principal component analysis;
support vector machine;
wavelet packet entropy
- From:
Journal of Biomedical Engineering
2018;34(6):831-836
- CountryChina
- Language:Chinese
-
Abstract:
Focused on the world-wide issue of improving the accuracy of emotion recognition, this paper proposes an electroencephalogram (EEG) signal feature extraction algorithm based on wavelet packet energy entropy and auto-regressive (AR) model. The auto-regressive process can be approached to EEG signal as much as possible, and provide a wealth of spectral information with few parameters. The wavelet packet entropy reflects the spectral energy distribution of the signal in each frequency band. Combination of them gives a better reflect of the energy characteristics of EEG signals. Feature extraction and fusion are implemented based on kernel principal component analysis. Six emotional states from a public multimodal database for emotion analysis using physiological signals (DEAP) are recognized. The results show that the recognition accuracy of the proposed algorithm is more than 90%, and the highest recognition accuracy is 99.33%. It indicates that this algorithm can extract the feature of EEG emotion well, and it is a kind of effective emotion feature extraction algorithm, providing support to emotion recognition.
