Ayesha Sarwar¹, Kashif Javed¹, Muhammad Jawad Khan¹, Saddaf Rubab¹, Oh-Young Song^2,*, Usman Tariq³

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3825-3840, 2021, DOI:10.32604/cmc.2021.016893

Abstract Brain-Computer Interface (BCI) is a system that provides a link between the brain of humans and the hardware directly. The recorded brain data is converted directly to the machine that can be used to control external devices. There are four major components of the BCI system: acquiring signals, preprocessing of acquired signals, features extraction, and classification. In traditional machine learning algorithms, the accuracy is insignificant and not up to the mark for the classification of multi-class motor imagery data. The major reason for this is, features are selected manually, and we are not able to get those features that give… More >

Yuxi Jia¹, Feng Li^1,2, Fei Wang^1,2,*, Yan Gui^1,2,3

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 of enhancing the representation of… More >