Deep EEG super-resolution: Upsampling EEG spatial resolution with Generative Adversarial Networks
High-density EEG systems are prohibitively expensive, limiting spatial resolution in practical applications. To address this, we propose the first generative adversarial network (GAN)-based spatial super-resolution method for EEG, enabling end-to-end reconstruction from low-channel-count EEG signals to high-density channel configurations. Unlike conventional interpolation techniques, our approach explicitly models physiological inter-channel dependencies, jointly optimizing signal fidelity and neurophysiological plausibility. Evaluated on mental imagery task data, the method reduces MSE by approximately 104× and MAE by ~102× compared to bicubic interpolation. Critically, downstream classification accuracy remains virtually unchanged (drop <0.5%), demonstrating that reconstructed data preserve discriminative neural information. This work establishes a novel paradigm for cost-effective, high-resolution EEG acquisition without hardware modification.