Study on EEG Emotion Recognition Method Based on Self-supervised Graph Network

EEG emotion recognition refers to the technology of identifying human emotional states by analyzing electroencephalogram (EEG) signals,which has wide application prospects in some fields such as medical health,and human-computer interaction.Currently,EEG-based emotion recognition frequently relies on machine learning or deep learning techniques to thoroughly train labeled EEG data and differentiate various emotional states.However,such methods require a lot of data annotation,which is time-consuming and labor-intensive.Meanwhile,research shows that the spatial structure information of EEG signals can reflect the interaction of brain areas related to different emotional states,which can help identify emotional characteristics.To this end,this paper proposes an EEG emotion recognition method based on self-supervised graph network.First,the meiosis method is used to preprocess the EEG signal.Then,a graph convolutional network is used to extract spatial structure information from EEG signals,and the network is trained through self-supervised tasks.Finally,the feasibility and effectiveness of the proposed method have been validated through numerical experiments using the public datasets SEED and SEED-IV.Numerical results show that the accuracy of emotion recognition is 95.16% and 80.23%,which is superior to current methods.