Objective. The original motor imagery electroencephalography (MI-EEG) data contains not only temporal features but also a large number of spatial features related to the distribution of electrodes on the brain. However, in the process of MI-EEG decoding, most of the current convolutional neural network (CNN) based methods do not make the utmost of the spatial features related to electrode distribution. Approach. In this study, we adopt a concise 3D representation for the MI-EEG data to take full advantage of the spatial features and propose a two-branch 3D CNN (TB-3D CNN) for the 3D representation of MI-EEG data. First, the spatial and temporal features of the input 3D samples are extracted by the spatial and temporal feature learning branches, respectively, to avoid the mutual interference between the temporal and spatial features. Then, the central loss is introduced into the TB-3D CNN framework to further improve the MI-EEG decoding accuracy. And a 3D data augmentation method based on the cyclic translation of time dimension is proposed for the 3D representation method to alleviate the overfitting problem. Main results. Some experiments are conducted on the famous BCI competition IV 2a dataset to evaluate the effectiveness of the proposed MI-EEG decoding method. The experimental results comparison with some state-of-the-art methods demonstrates that the average accuracy of our method is 4.42% higher than that of the best of the comparative methods. Significance. The proposed MI-EEG decoding method has great promise to improve the performance of motor imagery brain-computer interface system.

客观的。原始运动图像脑电图（MI-EEG）数据不仅包含时间特征，还包含大量与大脑上电极分布相关的空间特征。然而，在 MI-EEG 解码过程中，目前大多数基于卷积神经网络 (CNN) 的方法并没有充分利用与电极分布相关的空间特征。方法。在这项研究中，我们对 MI-EEG 数据采用简洁的 3D 表示，以充分利用空间特征，并提出了一种用于 MI-EEG 数据的 3D 表示的两分支 3D CNN（TB-3D CNN）。首先，输入3D样本的空间和时间特征分别由空间和时间特征学习分支提取，以避免时间和空间特征之间的相互干扰。然后，将中心损失引入TB-3D CNN框架以进一步提高MI-EEG解码精度。并且针对3D表示方法提出了一种基于时间维度循环平移的3D数据增强方法来缓解过拟合问题。主要结果。在著名的 BCI 竞赛 IV 2a 数据集上进行了一些实验，以评估所提出的 MI-EEG 解码方法的有效性。与一些最先进方法的实验结果比较表明，我们的方法的平均准确度比最好的比较方法高 4.42%。意义。所提出的 MI-EEG 解码方法有望提高运动想象脑机接口系统的性能。