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

arXiv: https://arxiv.org/abs/2508.09242 · HTML: https://mlsanity.com/paper/2508.09242/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface](https://mlsanity.com/paper/2104.01233/) (2021)
- [Multi-Kernel Capsule Network for Schizophrenia Identification](https://mlsanity.com/paper/1907.12827/) (2019)

## Abstract (arXiv)

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.