Because of "the non-repeatability of the experiment settings and conditions"
and "the variability of brain patterns among subjects", the data distributions
across sessions and electrodes are different in cross-subject motor imagery
(MI) studies, eventually reducing the performance of the classification model.
Systematically summarised based on the existing studies, a novel
temporal-electrode data distribution problem is investigated under both
intra-subject and inter-subject scenarios in this paper. Based on the presented
issue, a novel bridging domain adaptation network (BDAN) is proposed, aiming to
minimise the data distribution difference across sessions in the aspect of the
electrode, thus improving and enhancing model performance. In the proposed
BDAN, deep features of all the EEG data are extracted via a specially designed
spatial feature extractor. With the obtained spatio-temporal features, a
special generative bridging domain is established, bridging the data from all
the subjects across sessions. The difference across sessions and electrodes is
then minimized using the customized bridging loss functions, and the known
knowledge is automatically transferred through the constructed bridging domain.
To show the effectiveness of the proposed BDAN, comparison experiments and
ablation studies are conducted on a public EEG dataset. The overall comparison
results demonstrate the superior performance of the proposed BDAN compared with
the other advanced deep learning and domain adaptation methods.

提出了一种基于跨主体运动想象的时间 - 电极数据分布问题的桥接域适应网络 (BDAN)，通过特殊设计的空间特征提取器提取所有脑电图数据的深层特征，建立起桥接领域，最小化跨会话和电极的差异，并自动传递已知知识，通过对公共脑电图数据集进行比较实验和消融研究，证明了 BDAN 相比其他先进的深度学习和域适应方法的出色性能。