EEG Emotion Recognition Based on Granger Causality and CapsNet Neural Network

Emotion recognition is a very challenging task in the brain-computer interface field, and it is of great significance in medical, education, military, and other fields. The classification problem is the key to the field of emotion recognition research. In this paper, a classification model based on CapsNet neural network is proposed. By extracting the granger causality feature of original EEG signals, sparse group lasso algorithm is used for feature screening, and the obtained high-relevance feature subset is taken as the input of the network to achieve the final emotional classification. The experimental results show that by adjusting the model parameters and network structure, the constructed CapsNet neural network performs emotional classification on EEG signals, and obtains 88.09% and 87.37% average classification accuracy under valence and arousal emotion dimensions, compared with SVM and CNN. The classification system can obtain better results and significantly improve the performance of EEG emotional classification. This is the first time that CapsNet is applied to EEG emotional classification.