Sam Nason-Tomaszewski, Ph.D.

Sam Nason-Tomaszewski is a postdoctoral fellow advised by Chethan Pandarinath in the Systems Neural Engineering lab at Emory University. His current research focuses on developing real-time brain-computer interfaces to recreate hand function in people with upper extremity paralysis. Specifically, he is using deep neural networks that predict high-fidelity arm, hand and finger movements in virtual environments using signals recorded by sensors implanted into the brain. Sam received his doctorate in biomedical engineering from Cindy Chestek’s lab at the University of Michigan where he investigated brain-controlled restoration of dexterous finger function in monkeys using functional electrical stimulation. He received the College of Engineering’s Towner Prize for Outstanding Ph.D. Research for his dissertation work. Prior to his doctoral studies, Sam received a Master of Science in biomedical engineering from the University of Michigan and a Bachelor of Science in electrical engineering from the University of Florida.

Principal Investigator: Chethan Pandarinath

Fellow: Nghi Ho

Undergraduate Fellow Project
People with paralysis have no avenues through which they can regain able-bodied function of their hands and fingers despite the circuits in the brain controlling movement often remaining intact. This project focuses on developing brain-computer interfaces to investigate how the brain generates dexterous hand and finger behaviors and find ways to restore such function to people with paralysis in a clinical trial. To do so, the researchers implant sensors into the motor cortex (the part of the brain that controls movement) of a person with paralysis. Then, they use artificial neural networks to study how biological neuron populations generate behavior and use them to predict the movements a person is trying to make in real-time to control a virtual hand. The SURF fellow joining this team will assist with developing the virtual hand model and machine learning algorithms. This project is ideal training for someone interested in real-time brain-computer interfaces and cutting-edge machine learning.