Before revision:  Specifically, our proposed method extracts subject-invariant and class-relevant features, thereby enhancing generalizability in overall classification. It is also noteworthy that our method can be applicable to a new subject with a small amount of data via fine-tuning, thus reducing calibration time for its practical uses. We validated our proposed method on two large motor imagery EEG datasets via comparisons with other competing methods .
After revision:  Specifically, our proposed method extracts subject-invariant and class-relevant and class-irrelevant ones, (2) to enrich class-discriminative feature representation. On two large EEG datasets, we validated the effectiveness of our proposed framework by comparing with several comparative methods in performance. Further, we conducted rigorous analyses by performing an ablation study in regard to the components in our network, explaining our model's decision on input EEG signals via layer-wise relevance propagation, and visualizing the distribution of learned features via t-SNE .