GCDE: Graph-Embedded Conditional Diffusion for EEG Data Augmentation

The inherent scarcity of high-quality electroencephalography (EEG) datasets critically constrains the development of robust brain-computer interface (BCI). Data augmentation has thus emerged as a crucial strategy for artificially enlarging the dataset. However, existing augmentation frameworks often struggle to generate highfidelity signals. In this paper, we propose a novel EEG data augmentation framework based on the Graph-Embedded Conditional Diffusion model for generating artificial EEG (GCDE) to augment dataset and improve the performance of EEG decoder. Unlike Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN), GCDE employs an iterative denoising process to generate realistic signals. We integrate Graph Embedding within a U-Net architecture to learn the spatial topological relationships among multiple EEG electrodes, thereby capturing the complex temporal and neuroscientific significance inherent EEG signals. Additionally, we incorporate label embedding to enable conditional, classspecific generation. We evaluate GCDE on the BCI Competition IV 2a dataset and investigate the optimal ratio that maximizes performance improvements. GCDE achieves significant improvements of 6.95% in EEGNet and 3.36% in the ST-GF model when the generated data constitutes 75% of the real data. To validate its generalizability, we further conduct experiments on the SEED (emotion recognition) and Fatigue (fatigue detection) datasets, where accuracy with EEGNet increase to 98.87%(+2.64%) and 93.81%(+3.60%), respectively. These comprehensive results demonstrate that GCDE is a powerful and generalizable framework for generating high-quality EEG signals, advancing data augmentation in BCI and other EEGbased domains. Our code will be released upon acceptance.