Attention-based Transfer Learning for Brain-computer Interface
This addresses brain-computer interface accuracy and automated brain area identification, but it is incremental as it builds on existing attention and transfer learning methods.
The paper tackled EEG classification by using attention-based transfer learning to weigh different brain functional areas, achieving state-of-the-art performance with empirical demonstrations.
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.