Emotion Recognition from EEG Using Rhythm Synchronization Patterns with Joint Time-Frequency-Space Correlation

Recently there has attracted wide attention in EEG-based emotion recognition (ER), which is one of the utilization of Brain Computer Interface (BCI). However, due to the ambiguity of human emotions and the complexity of EEG signals, the EEG-ER system which can recognize emotions with high accuracy is not easy to achieve. In this paper, by combining discrete wavelet transform, correlation analysis, and neural network methods, we propose an Emotional Recognition model based on rhythm synchronization patterns to distinguish the emotional stimulus responses to different emotional audio and video. In this model, the entire scalp conductance signal is analyzed from a joint time-frequency-space correlation, which is beneficial to the depth learning and expression of affective pattern, and then improve the accuracy of recognition. The accuracy of the proposed multi-layer EEG-ER system is compared with various feature extraction methods. For analysis results, average and maximum classification rates of 64% and 67.0% were obtained for arousal and 66.6% and 76.0% for valence.