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, and real-time systems) to advance the performance of brain-machine interfaces and neuromodulatory devices.
Contact: chethan [at] gatech [dot] edu
Artificial intelligence could be the key to faster, universal interfaces for paralyzed patients
BME researcher plans to engineer immune cells to become lifelong cancer sentinels
The approach is simple, cheap, and even can quantify antibody levels in the body