Research on electroencephalogram emotion recognition based on the feature fusion algorithm of auto regressive model and wavelet packet entropy / 生物医学工程学杂志
Journal of Biomedical Engineering
; (6): 831-836, 2018.
Article
en Zh
| WPRIM
| ID: wpr-771103
Biblioteca responsable:
WPRO
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.
Texto completo:
1
Índice:
WPRIM
Tipo de estudio:
Prognostic_studies
Idioma:
Zh
Revista:
Journal of Biomedical Engineering
Año:
2018
Tipo del documento:
Article