EEG Emotion Recognition Based on Hybrid Neural Networks

A hybrid neural network (3DCNN-BLSTM) based on a 3-Dimensional Convolutional Neural Network (3D-CNN) combined with a Bi-directional Long Short-term Memory Neural Network (BLSTM) is proposed to preserve the spatial information of the EEG while taking full advantage of its time-related information. Emotion re-cognition experiments on DEAP and SEED datasets are carried out to evaluate the classification performance of the model. The experiment results show that the 3DCNN-BLSTM model can effectively learn the correlation between EEG multi-channels and time dimension information and improve the performance of emotion classification. The ave-rage accuracy of emotion recognition of arousal and valence in the two-classification experiments on DEAP dataset are 93.56% and 93.21% respectively; the average accuracy of emotion recognition in the four-classification experiments on DEAP dataset is 90.97%; and the average accuracy of emotion recognition in the three-classification experiments on SEED dataset is 98.90%.