The possibility of unifying neural interfaces to create an integrated control system for prostheses: a brief review

Abstract

The purpose of research. To date, neurointerfaces have not been unified to create combined prosthetic control systems. Based on this, this review is aimed at understanding the possibility of integrating neurointerfaces by clarifying the advantages and disadvantages of neurotechnologies related to prosthetics and the possible creation of a combined prosthesis control system.Methods. Analysis of brain-computer interfaces available in the literature in combination with neuroimaging experiments, especially in a hybrid system. A number of databases of scientific literature were used for the analysis, namely Google Scholar, scopus, etc. Links to the database data on the Internet: https://scholar.google.com/, https://www.mdpi.com/journal/sensors, elibrary.ru, https://www.refseek.com, https://link.springer.com/, https://www.base-search.netResults. Brain-computer interfaces are currently being used in a wide variety of fields, including to improve the lives of people with disabilities. However, individual neural interfaces have certain disadvantages that make it difficult to use them to control mechanical devices, including prosthetic limbs. Hybrid neural interface systems (as an integrated software and hardware complex) are significantly superior to those obtained using separate neural interfaces, and these systems can be used for medical purposes.Conclusion. This review provides a brief overview of the disability of people with missing upper limbs and how to improve their lives with prosthetics. The analysis of various hybrid methods of brain research is given. It can be noted that fNIRS technology is the closest technology that can facilitate the integration of neural interfaces, since it has advantages that make it a tool that complements other technologies, its advantages make up for the inherent disadvantages of fNIRS. It has been established that the hybrid system provides a clear advantage over individual neural interfaces.