A Novel Multidimensional Feature Extraction Method Based on VMD and WPD for Emotion Recognition

Emotion plays an indispensable role in the process of human cognition and decision-making, but it is often neglected in human-computer interaction (HCI). Although many researchers have applied some decomposition algorithms to extract features from EEG, there are several shortcomings including the modal aliasing, a high computational cost and so on. In this paper, we introduce variational mode decomposition (VMD) and wavelet packet decomposition (WPD) algorithms and propose a multidimensional feature extraction method based on VMD and WPD for emotion recognition. Firstly, we apply VMD to decompose the EEG signal into a specific number of variational mode functions (VMF). Secondly, WPD is executed to generate an emotional frequency band. Then, we continue to extract the wavelet packet entropy (WPE), modified multi-scale sample entropy (MMSE), fractal dimension (FD) and first difference (1ST) of each emotional VMF to construct a new feature form. Finally, the random forest (RF) is utilized to classify the extracted emotional states. The experiment results demonstrate that the proposed method is more competitive and universal for emotion recognition, which provides a novel idea for the application of multidimensional feature extraction.