Using motor imagery for movement intention detection in individuals with limb motor function impairments (literature review)

Brain-Computer Interfaces (BCI) represent a promising field in neuroengineering and rehabilitation medicine, particularly for patients with motor function impairments caused by limb amputations or neurological injuries. One of the key approaches in this domain is the use of Motor Imagery (MI) to identify motor intentions, enabling effective control of prostheses and neurotechnological devices. This review analyzes current methods of integrating MI into rehabilitation processes, specifically focusing on its application in restoring motor functions after amputation. Special attention is given to the mechanisms of motor intention detection using ElectroEncephaloGraphy (EEG), particularly Event-Related Desynchronization (ERD) and Event-Related Synchronization (ERS), which are primary features of sensorimotor rhythms during imagined movement. The review considers experimental studies evaluating the effectiveness of MI in rehabilitation and prosthetic control. It examines the use of MI to reduce phantom limb pain, improve somatosensory cortical organization, and control lower limb prostheses, providing a natural sense of movement even in complex conditions. Experimental data also suggest that integrating MI with Virtual Reality (VR) enhances patient motivation and reduces physical strain during rehabilitation. Key challenges discussed include the low signal-to-noise ratio in EEG and individual differences in patients' MI abilities. The analysis confirms that MI technologies can significantly improve the quality of life for individuals with motor function impairments, promoting both physical and cognitive recovery. Prospective research directions include optimizing motor intention recognition algorithms, enhancing EEG interpretation accuracy, and expanding the potential of MI in controlling complex prosthetic systems. Keywords: Brain-Computer Interface, neuroplasticity, post-amputation rehabilitation, movement intention prediction, phantom limb pain.