论文标题
基于卷积神经网络的运动图像和SSVEP的混合脑机构接口
A Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network
论文作者
论文摘要
基于脑电图（EEG）的脑部计算机界面（BCI）的关键在于神经解码，并且可以通过使用混合BCI范式（即融合多个范式）来提高其精度。但是，混合BCI通常需要在每个范式中的EEG信号中单独的处理过程，这大大降低了EEG特征提取的效率和模型的普遍性。在这里，我们提出了一个基于两流卷积神经网络（TSCNN）的混合脑计算机界面。它结合了稳态的视觉诱发电位（SSVEP）和运动图像（MI）范式。 TSCNN自动学习在训练过程中的两个范式中提取EEG特征，并将解码精度提高25.4％，而与测试数据中的SSVEP模式相比，与SSVEP模式相比2.6％。此外，TSCNN的多功能性得到了验证，因为它在单模中提供了相当大的性能（MI的70.2％，SSVEP为93.0％）和混合模式方案（MI-SSVEP Hybrid）。我们的工作将促进基于EEG的BCI系统的现实应用。
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.
