DiffEEGLossNet: A Single-Step Diffusion Framework for Motor Imagery EEG Generation

Motor imagery-based brain-computer interfaces (MI-BCIs) require abundant high-quality EEG data, yet collecting such data remains challenging. Recent diffusion probabilistic models (DPMs) have shown strong capabilities in generating realistic data, yet synthesizing EEG signals that preserve neuroscientific relevance remains challenging. To address this issue, we propose DiffEEGLossNet, a diffusion-based generative framework tailored for multi-channel EEG synthesis. By introducing an ERD/ERS-informed loss function, the model constrains the diffusion process to maintain motor imagery-specific neural patterns, enhancing both physiological plausibility and data fidelity. Experiments on the BCI Competition IV 2a and 2b datasets demonstrate that the generated EEG signals not only exhibit high realism and consistency but also significantly boost the performance of downstream EEG classification models through effective data augmentation.