Case Study: Battelle helps restore touch and movement for Ian Burkhart with MathWorks-powered brain-computer interface

Reconnecting the Brain After Paralysis Using Machine Learning

Battelle collaborated with The Ohio State University Wexner Medical Center on a research project with the challenge of restoring motor function and a sense of touch to an individual, Ian Burkhart, who was paralyzed by a spinal cord injury. The goal was to create a brain-computer interface (BCI) that could bypass the damaged nervous system to allow conscious control of a paralyzed hand and provide sensory feedback.

Using MATLAB, Battelle developed machine learning algorithms to decode neural signals from an implanted chip. This NeuroLife system translated thoughts into commands to stimulate hand muscles via an electrode sleeve. Furthermore, the team used MATLAB to build algorithms that detected residual touch signals in the brain, routing this information to a vibrotactile band on the user's bicep. This solution successfully restored the ability to grip objects with over 90% accuracy without visual cues, dramatically increasing the user's confidence and independence while using the system.

Battelle

Ian Burkhart

Battelle