A code-modulated visual evoked potential (cVEP) brain-computer interface dataset from 30 healthy participants performing offline and online spelling tasks. This derivative dataset demonstrates a calibration-free BCI approach using an encoding model that systematically reduces training data requirements, ultimately achieving high communication rates without any user-specific calibration data. The study validates neural encoding models as an alternative to traditional event-related potential templates for practical plug-and-play BCI applications.