BCI Application in Stroke Rehabilitation: Robotic Assisted System

Abstract

Stroke remains a pervasive global health concern, exacting enduring motor, cognitive, and emotional tolls on affected individuals. The evolving landscape of neural interfaces has recently shown promising strides in reinstating lost sensorimotor functions. Non-invasive brain-computer interfaces (BCIs) have gained widespread recognition for their inherent advantages—simplicity, safety, and cost-effectiveness. The ongoing refinement of BCIs involves addressing previous technological and neurophysiological limitations, focusing on understanding distinctive neurophysiological alterations observed in individuals with disabilities. The exploration of brain connectivity attributes is pivotal in implementing BCI-based control systems. Emerging as a notable prospect for post-stroke motor rehabilitation, Brain-Computer Interface (BCI)-based therapy has made significant progress in restoring sensorimotor function. Revolutionary advancements have been made to traditional brain-computer interface (BCI) systems, which rely on brain signals obtained from electroencephalography (EEG) and rule-based translation algorithms. This comprehensive review delves into the realm of BCI Robotic Assisted Systems, offering a nuanced analysis of contributions from diverse researchers. It meticulously dissects these systems' advantages and limitations, providing valuable insights into their efficacy. Ongoing endeavors within the field prioritize enhancing the portability, simplicity, and cost-effectiveness of BCI technology, ultimately refining its overall usability. As research progresses, emphasizing larger sample sizes, there is a tangible potential to augment the reliability of BCI systems for stroke rehabilitation significantly. This trajectory holds promise for extending the benefits of BCI technology to a broader spectrum of patients, marking a transformative leap in neurorehabilitation methodologiest.