Emotion recognition of EEG signals based on variational mode decomposition and weighted cascade forest

Emotion recognition is of a great significance in intelligent medical treatment and intelligent transportation. With the development of human-computer interaction technology, emotion recognition based on Electroencephalogram (EEG) signals has been widely concerned by scholars. In this study, an EEG emotion recognition framework is proposed. Firstly, variational mode decomposition (VMD) is used to decompose the nonlinear and non-stationary EEG signals to obtain intrinsic mode functions (IMFs) at different frequencies. Then sliding window tactic is used to extract the characteristics of EEG signals under different frequency. Aiming at the issue of feature redundancy, a new variable selection method is proposed to improve the adaptive elastic net (AEN) by the minimum common redundancy maximum relevance criterion. Weighted cascade forest (CF) classifier is constructed for emotion recognition. The experimental results on the public dataset DEAP show that the valence classification accuracy of the proposed method reaches 80.94%, and the classification accuracy of arousal is 74.77%. Compared with some existing methods, it effectively improves the accuracy of EEG emotion recognition.