This research develops advanced brain-machine interface systems to improve life for spinal cord injury patients. Using neural networks such as FinNet and dynamic recurrent neural decoders, the work aims to better extract and translate brain activity into movement while creating low-power hardware capable of supporting long-term practical neuroprosthetic applications.

This research improves neural implants for vision restoration by reproducing natural brain activity patterns. Using a two-way stimulation approach in the retina, electrical signals are optimized to activate neurons precisely. This enables more accurate visual perception, moving beyond crude light flashes toward meaningful vision, with potential to restore recognition of familiar faces.