There is a correlation between adjacent channels of electroencephalogram (EEG), and how to represent this correlation is an issue that is currently being explored. In addition, due to inter-individual differences in EEG signals, this discrepancy results in new subjects need spend a amount of calibration time for EEG-based motor imagery brain-computer interface. In order to solve the above problems, we propose a Dynamic Domain Adaptation Based Deep Learning Network (DADL-Net). First, the EEG data is mapped to the three-dimensional geometric space and its temporal-spatial features are learned through the 3D convolution module, and then the spatial-channel attention mechanism is used to strengthen the features, and the final convolution module can further learn the spatial-temporal information of the features. Finally, to account for inter-subject and cross-sessions differences, we employ a dynamic domain-adaptive strategy, the distance between features is reduced by introducing a Maximum Mean Discrepancy loss function, and the classification layer is fine-tuned by using part of the target domain data. We verify the performance of the proposed method on BCI competition IV 2a and OpenBMI datasets. Under the intra-subject experiment, the accuracy rates of 70.42% and 73.91% were achieved on the OpenBMI and BCIC IV 2a datasets.

翻译：脑电图(EEG)相邻通道间存在相关性，如何表征这种相关性是目前正在探索的问题。此外，由于脑电信号的个体差异，导致新用户在使用基于脑电的运动想象脑机接口时需要花费大量校准时间。为解决上述问题，我们提出一种基于动态域自适应的深度学习网络(DADL-Net)。首先，将脑电数据映射至三维几何空间，通过3D卷积模块学习其时空特征，继而利用空间通道注意力机制强化特征，最终卷积模块可进一步学习特征的时空信息。最后，为应对跨被试与跨session差异，我们采用动态域自适应策略，通过引入最大均值差异损失函数减小特征间距离，并利用部分目标域数据对分类层进行微调。在BCI竞赛IV 2a与OpenBMI数据集上验证了所提方法的性能。在受试者内实验中，OpenBMI与BCI竞赛IV 2a数据集分别达到70.42%与73.91%的准确率。