MS-STGAN: A dual-branch multi-scale spatio-temporal generative adversarial framework for incomplete EEG-based emotion recognition

Electroencephalography (EEG) enables high-resolution emotion recognition but often suffers from incomplete data in real-world scenarios due to sensor failures or preprocessing errors. To this end, we propose a Multi-Scale Spatio-Temporal Generative Adversarial Network (MS-STGAN). Specifically, we first apply random masking to the EEG channels data to simulate missing data conditions in practical environments. Then, we design a spatio-temporal dual-branch generator to reconstruct complete representations: the spatial branch employs graph convolutional networks (GCNs) to capture robust inter-regional dependencies, while the temporal branch leverages the BiMamba state space model to encode the dynamic evolution of emotions. To further enhance feature learning, multi-scale 2D convolution and deconvolution layers are incorporated before and after both branches, enabling the extraction of diverse spatio-temporal features. Additionally, we introduce a generative adversarial framework, where the generator restores informative features from incomplete inputs and the discriminator enforces the authenticity of reconstructed data. Finally, a fusion module integrates the outputs of both branches for downstream classification. Extensive experiments on the DEAP and SEED-IV datasets validate the effectiveness of each component and demonstrate that MS-STGAN achieves superior performance and strong generalization ability.