Cross-BCI, A Cross-BCI-Paradigm Classifica-tion Model Towards Universal BCI Applications

Classification models used in brain-computer interface (BCI) are usually designed for a single BCI paradigm. This requires the redevelopment of the model when applying it to a new BCI paradigm, resulting in repeated costs and effort. Moreover, less complex deep learning models are desired for practical usage, as well as for deployment on portable devices. In or-der to fill the above gaps, we, in this study, proposed a light-weight and unified decoding model for cross-BCI-paradigm classification. The proposed model starts with a tempo-spatial convolution. It is followed by a multi-scale local feature selec-tion module, aiming to extract local features shared across BCI paradigms and generate weighted features. Finally, a mul-ti-dimensional global feature extraction module is designed, in which multi-dimensional global features are extracted from the weighted features and fused with the weighted features to form high-level feature representations associated with BCI para-digms. The results, evaluated on a mixture of three classical BCI paradigms (i.e., MI, SSVEP, and P300), demon-strate that the proposed model achieves 88.39%, 82.36%, 80.01%, and 0.8092 for accuracy, macro-precision, mac-ro-recall, and macro-F1-score, respectively, significantly out-performing the compared models. This study pro-vides a feasible solution for cross-BCI-paradigm classifica-tion. It lays a technological foundation for de-veloping a new generation of unified decoding systems, paving the way for low-cost and universal practical applications.