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The electroencephalography classifier is the most important component ofbrain-computer interface based systems. There are two major problems hinderingthe improvement of it. First, traditional methods do not fully exploitmultimodal information. Second, large-scale annotated EEG datasets are almostimpossible to acquire because biological data acquisition is challenging andquality annotation is costly. Herein, we propose a novel deep transfer learningapproach to solve these two problems. First, we model cognitive events based onEEG data by characterizing the data using EEG optical flow, which is designedto preserve multimodal EEG information in a uniform representation. Second, wedesign a deep transfer learning framework which is suitable for transferringknowledge by joint training, which contains a adversarial network and a specialloss function. The experiments demonstrate that our approach, when applied toEEG classification tasks, has many advantages, such as robustness and accuracy.

Deep Transfer Learning for EEG-based Brain Computer Interface