  The electroencephalography classifier is the most important component of
brain-computer interface based systems. There are two major problems hindering
the improvement of it. First, traditional methods do not fully exploit
multimodal information. Second, large-scale annotated EEG datasets are almost
impossible to acquire because biological data acquisition is challenging and
quality annotation is costly. Herein, we propose a novel deep transfer learning
approach to solve these two problems. First, we model cognitive events based on
EEG data by characterizing the data using EEG optical flow, which is designed
to preserve multimodal EEG information in a uniform representation. Second, we
design a deep transfer learning framework which is suitable for transferring
knowledge by joint training, which contains a adversarial network and a special
loss function. The experiments demonstrate that our approach, when applied to
EEG classification tasks, has many advantages, such as robustness and accuracy.
