Yuxi Jia1, Feng Li1,2, Fei Wang1,2,*, Yan Gui1,2,3
Journal of Information Hiding and Privacy Protection, Vol.1, No.1, pp. 11-21, 2019, DOI:10.32604/jihpp.2019.05979
Abstract The Brain-Computer Interfaces (BCIs) had been proposed and used in therapeutics for decades. However, the need of time-consuming calibration phase and the lack of robustness, which are caused by little-labeled data, are restricting the advance and application of BCI, especially for the BCI based on motor imagery (MI). In this paper, we reviewed the recent development in the machine learning algorithm used in the MI-based BCI, which may provide potential solutions for addressing the issue. We classified these algorithms into two categories, namely, and enhancing the representation and expanding the training set. Specifically, these methods More >
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