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. In previous work, 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. Our focus has been two-fold: