# A Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network

arXiv: https://arxiv.org/abs/2212.05289 · HTML: https://mlsanity.com/paper/2212.05289/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 10 Dec 2022.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Concrete Dropout](https://mlsanity.com/paper/1705.07832/) (2017)
- [Activation Functions: Comparison of trends in Practice and Research for Deep Learning](https://mlsanity.com/paper/1811.03378/) (2018)
- [Comparison of non-linear activation functions for deep neural networks on MNIST classification task](https://mlsanity.com/paper/1804.02763/) (2018)
- [Embedding Decomposition for Artifacts Removal in EEG Signals](https://mlsanity.com/paper/2112.00989/) (2021)
- [Transfer learning to decode brain states reflecting the relationship between cognitive tasks](https://mlsanity.com/paper/2206.03950/) (2022)
- [Boosting Template-based SSVEP Decoding by Cross-domain Transfer Learning](https://mlsanity.com/paper/2102.05194/) (2021)
- [Partial Least Square Regression via Three-factor SVD-type Manifold Optimization for EEG Decoding](https://mlsanity.com/paper/2208.04324/) (2022)

## Built on by (most cited first, 2 of 2)

- [Uncovering cognitive taskonomy through transfer learning in masked autoencoder-based fMRI reconstruction](https://mlsanity.com/paper/2407.00033/) (2024)
- [SwitchBraidNet: Quantisation-Aware Lightweight Architecture for Hybrid Brain-Computer Interface](https://mlsanity.com/paper/2606.18816/) (2026)

## Abstract (arXiv)

The key to electroencephalography (EEG)-based brain-computer interface (BCI) lies in neural decoding, and its accuracy can be improved by using hybrid BCI paradigms, that is, fusing multiple paradigms. However, hybrid BCIs usually require separate processing processes for EEG signals in each paradigm, which greatly reduces the efficiency of EEG feature extraction and the generalizability of the model. Here, we propose a two-stream convolutional neural network (TSCNN) based hybrid brain-computer interface. It combines steady-state visual evoked potential (SSVEP) and motor imagery (MI) paradigms. TSCNN automatically learns to extract EEG features in the two paradigms in the training process, and improves the decoding accuracy by 25.4% compared with the MI mode, and 2.6% compared with SSVEP mode in the test data. Moreover, the versatility of TSCNN is verified as it provides considerable performance in both single-mode (70.2% for MI, 93.0% for SSVEP) and hybrid-mode scenarios (95.6% for MI-SSVEP hybrid). Our work will facilitate the real-world applications of EEG-based BCI systems.