Before revision:  Importantly, the model generalizes well across users: when trained on data pooled from previous users, it can successfully perform on new users. Thus, the time-consuming BCI calibration can be omitted. Moreover, the model can be incrementally updated, resulting in +8.9\% average accuracy improvement in offline experiments and +17.0\% in a real-time BCI. We argue that this model can be used in practical BCIs and basic neuroscience research .
After revision:  Importantly, the model is robust to inter-individual differences and can successfully generalize to new subjects in offline and online classification .