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  • Open Access

    ARTICLE

    Analysis of Collaborative Brain Computer Interface (BCI) Based Personalized GUI for Differently Abled

    M. Umaa,c, T. Sheelab

    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 747-757, 2018, DOI:10.1080/10798587.2017.1332804

    Abstract Brain-Computer Interfaces (BCI) use Electroencephalography (EEG) signals recorded from the brain scalp, which enable a communication between the human and the outside world. The present study helps the patients who are people locked-in to manage their needs such as accessing of web url’s, sending/receiving sms to/from mobile device, personalized music player, personalized movie player, wheelchair control and home appliances control. In the proposed system, the user needs are designed as a button in the form of a matrix, in which the main panel of rows and columns button is flashed in 3 sec intervals. Subjects… More >

  • Open Access

    ARTICLE

    A Survey on Machine Learning Algorithms in Little-Labeled Data for Motor Imagery-Based Brain-Computer Interfaces

    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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