Deep Learning Techniques for EEG-Based BCI: Analysis and Applications

Brain-Computer Interface enables people to communicate with each other or with computing systems without any movements even speech. Recent advancements in BCI mostly use EEG as their channels to achieve connections between the brain and computer, leaving EEG signal decoding a challenge to be solved. Similar complex problems have been given satisfying solutions using Deep Learning techniques, due to its capacity to learn patterns hidden in raw data and its ability to be generalized. However, at present, people usually use some methods that are not state-of-the-art DL architectures for EEG decoding. Besides, some open challenges and limitations have also been exposed in the previous research yet haven’t been solved. In this paper, some basic but critical concepts and challenges in EEG-Based BCI systems are mentioned, as well as prospects of further development in combination with DL and EEG analysis are arisen. The author expects that the investigation presented in this paper will help researchers to have the basic and hands-on concepts for constructing EEG-Based BCI and prompt more recent DL techniques to be used in designing an effective EEG-BCI system.