Review of research in the direction of EEG-based control method for bionic prosthesis

Abstract

This article provides an overview of contemporary research in the direction of controlling bionic prostheses using electroencephalography (EEG), which is an important and promising field in the rehabilitation sphere. The use of intuitive and intelligent control methods based on EEG enables significant restoration of upper limb functionality in patients who have lost limbs due to injuries or diseases. The results of numerous studies demonstrating the effectiveness of EEG-based systems for controlling bionic prostheses are analyzed. Special attention is given to the impact of sensor placement and differentiation between intramuscular and surface EEG. A significant portion of the article is devoted to reviewing methods used for decoding movement intentions and their subsequent interpretation for prosthesis control. Among these methods, machine learning and deep learning algorithms stand out for their high accuracy and signal processing speed. Additionally, research combining EEG with other methods, such as electrooculography (EOG), to enhance the reliability and safety of control systems is examined. It is found that EEG-based methods have great potential for implementing effective and intuitive bionic prosthesis control, opening up new possibilities in the rehabilitation of patients with upper limb disabilities. Further research and development in this field will contribute to the creation of more precise, faster, and more reliable control systems, which will better integrate bionic prostheses into users' everyday lives, significantly improving their quality of life and autonomy.