A novel brain inception neural network model using EEG graphic structure for emotion recognition

Abstract

Purpose EEG analysis of emotions is greatly significant for the diagnosis of psychological diseases and brain-computer interface (BCI) applications. However, the applications of EEG brain neural network for emotion classification are rarely reported and the accuracy of emotion recognition for cross-subject tasks remains a challenge. Thus, this paper proposes to design a domain invariant model for EEG-network based emotion identification. Methods A novel brain-inception-network based deep learning model is proposed to extract discriminative graph features from EEG brain networks. To verify its efficiency, we compared our proposed method with some commonly used methods and three types of brain networks. In addition, we also compared the performance difference between the EEG brain network and EEG energy distribution for emotion recognition. Result One public EEG-based emotion dataset (SEED) was utilized in this paper, and the classification accuracy of leave-one-subject-out cross-validation was adopted as the comparison index. The classification results show that the performance of the proposed method is superior to those of the other methods mentioned in this paper. Conclusion The proposed method can capture discriminative structural features from the EEG network, which improves the emotion classification performance of brain neural networks.

Keywords

• emotion recognition
• brain network
• eeg
• deep learning