Electroencephalography (EEG) data are pivotal in brain-computer interfaces (BCIs), yet their utility is hindered by data scarcity arising from high acquisition costs, noise susceptibility, and privacy constraints. Traditional augmentation methods, such as noise injection and signal transformations, often fail to preserve task-relevant structure in multichannel EEG, while deep generative models may suffer from mode collapse or produce physiologically inconsistent samples. To address these limitations, we propose a Riemannian Conditional Generative Adversarial Network (RC-GAN) that enforces geometric consistency during signal generation. RC-GAN leverages the manifold of symmetric positive definite (SPD) covariance matrices to regularize synthetic EEG trials according to covariance-based representations widely used in BCI decoding. Evaluated on the BNCI 2014-001 motor imagery dataset, the proposed method outperforms state-of-the-art augmentation techniques, achieving a 12.0% improvement in classification accuracy. Qualitative and quantitative analyses demonstrate that RC-GAN generates diverse and realistic EEG samples while enhancing robustness at different augmentation levels. These results highlight the benefit of incorporating Riemannian structure into generative models for EEG augmentation and provide a principled framework for improving the reliability of BCI systems.