A Multidomain Coupled Spatiotemporal Feature Interaction Model for EEG Emotion Recognition

Electroencephalogram (EEG) is a powerful tool for monitoring the brain’s electrical activity, providing valuable insights into an individual’s emotional state. The task of emotion recognition from EEG signals has gained significant attention, particularly with the advent of deep learning techniques. However, challenges such as the inherent instability of EEG signals and insufficient feature extraction methods can hinder effective recognition. In this study, we propose a novel approach for EEG emotion recognition—the multidomain coupled spatiotemporal feature interaction model (MCSFIM). First, leveraging the energy aggregation characteristics of the fractional Fourier transform (FrFT), we extract the features of the EEG signal fractional power spectral density (FrPSD) from multidomain to address the nonstationarity of EEG signals and construct a more comprehensive feature set. Then, by integrating graph convolutional networks (GCNs) and bidirectional long short-term memory (BiLSTM) networks, we capture the spatial topology and temporal dependencies of EEG signals for feature interaction and classification. To further enhance classification accuracy, we propose a dynamically weighted loss (DWL) function to reduce interclass imbalance and achieve more precise emotion recognition. Extensive experimental results on the DEAP and SEED datasets demonstrate that the proposed method outperforms other state-of-the-art methods.