Key result
An EEG emotion recognition framework using variational mode decomposition and a weighted cascade forest classifier achieved a valence classification accuracy of 80.94% and arousal accuracy of 74.77%.
Why the study?
Emotion recognition is significant in intelligent medical treatment and transportation, and developing effective EEG-based recognition methods addresses challenges with nonlinear signals and feature redundancy.
The proposed EEG emotion recognition framework using VMD and weighted cascade forest achieved high classification accuracy for valence and arousal on the DEAP dataset.
Proposed VMD-based EEG framework may aid emotion recognition in medical settings; leaves open validation and extension to cardiovascular populations.
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.
No takes yet. Share an insight, caveat, or question.
Xu et al. (2022) studied Emotion recognition. EEG emotion recognition framework (VMD and weighted cascade forest) vs. Existing methods was evaluated on Valence and arousal classification accuracy. An EEG emotion recognition framework using variational mode decomposition and a weighted cascade forest classifier achieved a valence classification accuracy of 80.94% and arousal accuracy of 74.77%.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: