
Neuroengineering, Deep learning, Brain-Machine Interfaces, Dynamical Systems, Motor physiology, Systems Neuroscience, Computational Neuroscience, Neural Coding
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