Our work centers on understanding how the brain represents information and intention, and using this knowledge to develop high-performance, robust, and practical assistive devices for people with disabilities and neurological disorders. We take a dynamical systems approach to characterizing the activity of large populations of neurons, combined with rigorous systems engineering (signal processing, machine learning, control theory, real-time system design) to advance the performance of brain-machine interfaces and neuromodulatory devices.
Intracortical neural prosthetics for people with paralysis
Our clinical work focuses on the development of Brain-Machine Interfaces for people with paralysis.
In previous work, through the BrainGate2 clinical trial, we used intracortical microelectrode arrays to extract neural activity from the brains of research participants with paralysis. By decoding this activity, we can decipher the person’s intention and use it to control assistive devices.
My focus has been two-fold:
- Improving the performance of neural prosthetic systems, including the development of decoding algorithms and signal processing strategies to best extract the user’s intention from their neural activity. This work demonstrated both the highest performance control quality and the highest communication rates for people with tetraplegia controlling neural prosthetics.
- Investigating the structure of neural population activity in motor cortical areas. We take a dynamical systems approach to characterizing the activity of large populations to understand how populations work cooperatively to drive movements. This work demonstrated, for the first time, that activity in human motor cortex evolves over time by following a consistent set of rules, regardless of the particular movement being attempted.