Bidirectional neural interfacing with M1-S1 for the restoration of somatosensation in people with paralysis We develop bidirectional brain–computer interfaces linking motor (M1) and somatosensory (S1) cortex to restore both movement and sensation in people with paralysis. We record and decode neural activity from motor and somatosensory areas to infer intended movements and control neuroprosthetic devices, while delivering patterned electrical stimulation to S1 to convey arti

Engineering medical devices for restoring sensorimotor functions in people with disability We develop mechatronic systems for restoring sensorimotor function, with a focus on peripheral nerve interfaces, prosthetic limbs, and wearable robotic systems. Our work combines advanced actuation, embedded sensing, control, and human–machine interfaces to create devices that can respond naturally to the user’s intended movements and physical interactions. For peripheral neuroprosthese

Develop computational models to improve human-machine link and to unveil the underlying mechanisms We develop computational and hybrid models of the peripheral and central nervous systems to improve the interaction between humans and neuroprosthetic technologies and to uncover the neural mechanisms underlying sensorimotor function. Our work combines data-driven, biophysical, and machine-learning approaches to model neural activity across multiple scales, from peripheral nerve