Code modulated Visually Evoked Potentials (cVEP) is an emerging paradigm for Brain-Computer Interfaces (BCIs) that offers reduced calibration times. However, cVEP-based BCIs still encounter challenges related to cross-session/subject variabilities. As Riemannian approaches have demonstrated good robustness to these variabilities, we propose the first study of deep Riemannian neural architectures, namely SPDNets, on cVEP-based BCIs. To evaluate their performance with respect to subject variabilities, we conduct classification tasks in a domain adaptation framework using a burst cVEP open dataset. This study demonstrates that SPDNet yields the best accuracy with single-subject calibration and promising results in domain adaptation.