The Brain-Computer Interface (BCI) system based on motor imagery (MI) is a hot research topic nowadays, which can control external devices through the brain and has a wide range of applications in rehabilitation, gaming, and entertainment. Due to the non-smooth, non-linear, and low signal-to-noise ratio of the MI EEG signal, it is challenging to accurately decode the MI task intention. A new end-to-end deep learning method is proposed to decode raw MI EEG signals without preprocessing, such as filtering and feature reinforcement. The 1D convolution is used to learn the time-frequency features in MI signals, and a four-branch fusion network is used as the main body to add a 1D CNN-AE block and 1D SE-block to enhance the algorithm's performance. Experiments on two publicly available datasets demonstrate that our proposed algorithm outperforms the current state-of-the-art methods. It achieves 86.11% and 89.51% on the BCI Competition IV-2a and the BCI Competition IV-2b datasets, respectively, and a 6.9% improvement in the generalizability test. The proposed data enhancement method can effectively alleviate the overfitting of the algorithm and improve the decoding performance. Further analysis shows that 1D convolution can effectively extract the features associated with the MI task.

基于运动想象（MI）的脑机接口（BCI）系统是当今的研究热点，它可以通过大脑控制外部设备，在康复、游戏和娱乐等领域有着广泛的应用。由于MI脑电信号的非平滑、非线性和低信噪比，准确解码MI任务意图具有挑战性。提出了一种新的端到端深度学习方法来解码原始 MI EEG 信号，而无需进行滤波和特征强化等预处理。利用一维卷积来学习MI信号中的时频特征，并以四分支融合网络为主体，添加一维CNN-AE块和一维SE-块来增强算法的性能。对两个公开可用数据集的实验表明，我们提出的算法优于当前最先进的方法。它在 BCI 竞赛 IV-2a 和 BCI 竞赛 IV-2b 数据集上分别达到 86.11% 和 89.51%，在泛化性测试中提高了 6.9%。所提出的数据增强方法可以有效缓解算法的过拟合，提高解码性能。进一步分析表明，一维卷积可以有效地提取与 MI 任务相关的特征。