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
Yue Chen and collaborators presenting their soft, flexible ‘hand’ at world’s top robotics conference
