  Different functional areas of the human brain play different roles in brain
activity, which has not been paid sufficient research attention in the
brain-computer interface (BCI) field. This paper presents a new approach for
electroencephalography (EEG) classification that applies attention-based
transfer learning. Our approach considers the importance of different brain
functional areas to improve the accuracy of EEG classification, and provides an
additional way to automatically identify brain functional areas associated with
new activities without the involvement of a medical professional. We
demonstrate empirically that our approach out-performs state-of-the-art
approaches in the task of EEG classification, and the results of visualization
indicate that our approach can detect brain functional areas related to a
certain task.
