  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.
