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.

我们提出了一种基于动态领域自适应的深度学习网络 (DADL-Net)，通过 3D 卷积模块将脑电数据映射到三维几何空间，并学习其时空特征，利用空间通道注意机制加强特征，最终通过卷积模块进一步学习特征的时空信息。通过引入最大均值差异损失函数，以及对目标域数据的部分微调，解决了脑机接口中相邻通道的相关性表示和个体差异的问题，在 BCI 竞赛 IV 2a 和 OpenBMI 数据集上验证了该方法的性能，分别达到 70.42% 和 73.91% 的准确率。