Open AccessSolving classification problems of visual evoked potentials for the brain-computer interfaces
Author(s) -
Vladimir Bulanov,
А. В. Захаров,
С. С. Чаплыгин
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/862/5/052051
Subject(s) - computer science , brain–computer interface , interface (matter) , evoked potential , signal (programming language) , domain (mathematical analysis) , artificial intelligence , electroencephalography , task (project management) , signal processing , machine learning , visual evoked potentials , pattern recognition (psychology) , human–computer interaction , digital signal processing , neuroscience , computer hardware , mathematical analysis , mathematics , management , bubble , maximum bubble pressure method , parallel computing , economics , biology , programming language
Development of the electroencephalogram-based neurocomputer interfaces requires application of the efficient algorithms for signal analysis. One of the methods of neurocomputer interface development is based on using single visual evoked potentials for characteristics control. However, it is a difficult task, requiring a combination of various methods of signal processing such as Blind Source Separation method, machine learning method and other modern mathematical and computational tools. In this paper, we drew a comparison between various classifiers for the visual evoked potentials recognition problem. The electroencephalogram records analyzed in this paper were published in the public domain.