Abstract
Motor Imagery (MI) is a cognitive exercise that can be utilized in motor rehabilitation. Using brain-computer interfaces (BCIs) is a practical technique to link computers and human brains, which can acquire and understand human brain signals when performing. MI-BCI has been proven effective to help the paralyzed regain basic movement controls. However, some problems are hindering the development of MI-BCI. Predecessors have proposed relevant methods to resolve them. Thus, it is important to learn about MI-BCI comprehensively and understand some novel methods to improve the performance of MI-BCI. Collecting and reviewing other essays, the study focuses on the structure and lists new applications of MI-BCI especially about the use of deep learning. The study also discusses self-paced training during the calibration phase and deep learning approaches for potential fields. Finally, the analysis of applications and four possible future directions are posed in the discussion, hoping to offer some useful advice to improve MI-based BCI.
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