Application of an effective neural network architecture based on deep learning algorithms for the development of a noninvasive neurocomputer interface

Abstract

Abstract. The article highlights the relevance of the development of modern noninvasive neurocomputer interfaces and identifies a problem in their development, which is the low accuracy of decoding human brain activity using modern noninvasive bidirectional neurocomputer interfaces, which makes it difficult to develop fully functioning noninvasive neuroprostheses. This problem is associated with a small number of domestic research in this area, as well as with an insufficient number of necessary tools for the development of this kind of neuroprostheses. The paper presents the principle of operation of this kind of interfaces, as well as varieties of neural interfaces. The scope of application of neurointerfaces and possible prospects for the development of this field are considered. The need to develop an artificial neural network using fuzzy logic aimed at improving the efficiency of isolating and filtering subtle signal patterns and structures of the human brain from the general signal background is justified.