Wavelet Packet Energy Features for EEG-Based Emotion Recognition

In this research, we present a new emotion recognition model using wavelet packet energy features using electroencephalography (EEG) data. Wavelet packets has been widely used as a means of time-frequency analysis of EEG in many different applications including brain computer interface systems. Features for emotion recognition are extracted from the EEG signals using a depth 6 wavelet packet tree. Wavelet packet energy of the sub-bands corresponding to delta (0-4Hz), theta (4-8Hz), alpha (8-13Hz), beta (13-30Hz), and gamma (30-49Hz) are taken as emotional features. Feature selection based on feature ranking is applied to select the most prominent EEG channel-frequency combinations for emotion recognition. Four classical classifiers such as linear discriminant analysis, support vector machine, K nearest neighbor and naive Bayes were used to detect the emotional states from the extracted features. To evaluate the effectiveness and validation of the proposed model, the SEED database has been employed. Based on the experiment results obtained, our method demonstrates that the LDA is more suitable for emotion recognition as compared to other classical classifier, which achieving the best average accuracy of 90.9386. Emotion recognition systems with high accuracy give opportunities to study real world applications such as mental state and fatigue monitoring.