Emotional EEG Prediction Based on Spatial-Temporal Graph Convolutional Neural Network

Background and Objective: Emotions are an integral part of our daily lives and have a significant impact on brain neural activities. However, most traditional studies on electroencephalogram (EEG) emotions have focused on identification and classification, without effectively predicting future EEG based on existing emotional EEG signals. This paper introduces a spatial-temporal graph convolutional neural network (STGCNN) to integrate temporal and spatial features of EEG, capturing more emotional information. Methods: After calculating the brain functional network operating Pearson correlation, it is utilized as the input information of the model together with the frequency domain differential entropy (DE). Information prediction in the spatiotemporal domain is mainly achieved through two cross structures of two temporal convolutions and one spatial convolution. Meanwhile, an ablation experiment is designed to illustrate the practicality of the model. Results and Conclusion: The performance of the model is evaluated on the SEED dataset for mixed and single emotions across three sessions and three frequency bands by metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Weighted Mean Absolute Percentage Error (WMAPE). All performance metrics confirm the predictability of EEG-based emotions, demonstrating the effectiveness of STGCNN for the prediction of EEG. Significance: Accurately emotional EEG prediction is crucial for brain neural rehabilitation training and forecasting.