Deep Transfer Learning for EEG-based Brain Computer Interface
This work addresses challenges in EEG-based brain-computer interfaces for applications like assistive technology, though it appears incremental as it builds on existing transfer learning methods.
The paper tackled the problems of insufficient multimodal information exploitation and lack of large-scale annotated EEG datasets in EEG-based brain-computer interfaces by proposing a deep transfer learning approach, resulting in improved robustness and accuracy in EEG classification tasks.
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.