As the lead intern on this research project, I explored how shared control systems can improve brain-computer interface (BCI)–driven neuroprosthetics by reducing cognitive load and enhancing movement efficiency. I led the literature analysis, created original visualizations, and contributed significantly to the poster presentation. My name was listed first among the interns in recognition of my leadership and primary contributions. Using R, I analyzed and visualized trends across 70+ articles, finding that EEG was the most frequently used method, followed by transformer models and cognitive state decoding, highlighting a convergence of neuroscience and machine learning. I also identified key publication venues, with Journal of Neural Engineering leading the field. This experience strengthened my data analysis and research communication skills and confirmed my interest in neuroscience, assistive technology, and human-centered healthcare innovation.