Optimizing Non-Invasive Brain-Computer Interfaces for Motor Rehabilitation in Individuals with Spinal Cord Injury

UC Santa Cruz | Mentored Research | 50+ Hours

Over the past several weeks, I had the opportunity to complete 50+ hours of mentored research at UC Santa Cruz, focusing on a field that sits at the intersection of neuroscience, engineering, and human health: non-invasive brain-computer interfaces (BCIs). Our project explored how BCIs can be optimized to improve motor rehabilitation for individuals with spinal cord injuries, an area of research with enormous potential to restore independence and transform lives.

What I Worked On

One of my primary contributions was applying R programming to analyze and visualize data from neural and rehabilitation studies. I cleaned and structured large datasets for meaningful analysis, created visualizations that revealed patterns in neural activity and motor recovery outcomes, and explored statistical models that tested how different BCI configurations influenced rehabilitation performance. These analyses helped us better understand the effectiveness of different signal processing approaches and guided conversations about how BCIs can be tuned to meet the specific needs of patients with spinal cord injuries.

Communicating the Research

To share our findings, I also created and presented a research poster that summarized the project’s methods, results, and implications. Developing the poster challenged me to translate complex data into a clear, compelling visual narrative, a skill that is essential in science communication. Presenting the poster gave me the opportunity to discuss the work with peers and mentors, strengthening my ability to engage diverse audiences in neuroscience research.

Why It Matters

Spinal cord injuries often leave individuals with limited mobility and few rehabilitation options. Non-invasive BCIs, which rely on external sensors rather than implanted devices, represent a more accessible, lower-risk pathway toward recovery. By combining neuroscience with data science, our work contributes to a growing body of knowledge about how to bridge the gap between brain signals and motor function in ways that can be applied clinically.

Beyond the Data

This research reinforced for me how computational tools like R are powerful drivers of medical innovation. Learning to transform raw neural data into insights that could one day support patient rehabilitation was both challenging and inspiring. It also deepened my appreciation for interdisciplinary research, the way coding, engineering, and medicine come together to create solutions that neither field could achieve alone.

Looking Ahead

This mentored research experience sharpened my technical skills in R programming and data analysis while also strengthening my commitment to pursuing projects that connect science and human impact. The process of developing and presenting a poster gave me experience in academic dissemination, a skill I plan to carry forward into future conferences and research collaborations.

Innovation in healthcare often begins at the intersection of disciplines. For me, this project was a reminder that code can be more than lines on a screen, it can be a bridge to human possibility.