A Multidimensional Feature Extraction Method Based on MSTBN and EEMD-WPT for Emotion Recognition from EEG Signals

Emotion recognition is an important component of human-computer interaction (HCI) systems. However, current emotion recognition methods have some drawbacks such as inconsistency in brain network size, lack of effective mining of features in different dimensions. In this paper, we propose a multidimensional feature extraction method based on MSTBN and EEMD-WPT for emotion recognition. Firstly, the wavelet packet transform (WPT) is utilized to decompose the pre-processed electroencephalography (EEG) signals into four frequency bands ($\theta,\alpha,\beta$, and $\gamma$), and phase locking value (PLV) is used to construct multi-band connectivity matrix. Secondly, to remove redundant information, the minimum spanning tree based brain network (MSTBN) is established and MSTBN features are extracted including global features and local features. Thirdly, ensemble empirical mode decomposition (EEMD) and WPT (EEMD-WPT) are applied to EEG signals for a more refined decomposition of modes and bands. Then, the modified multi-scale sample entropy (MMSE) and fractal dimension (FD) are extracted to capture the neural activity processes in the brain. Finally, the MSTBN features are fused with the nonlinear features MMSE and FD, which are input into random forest (RF) to identify emotions. Experimental results on DEAP dataset indicate that the accuracy is 87.24% and 89.84% for valance and arousal. Experimental analysis reveals that MSTBN of negative emotions is more divergent and emotional information is transmitted more rapidly in the brain. Women are more susceptible to emotional perception than men. The proposed multidimensional feature extraction method has potential to be applied to HCI systems.