Alex’s research at BrainGate focuses on detecting differences in recorded neural features based on the task context in which brain-computer interfaces are used. By examining differences in user-generated signals during completion of analogous tasks using different end-effectors, he hopes to find feature patterns that can help identify when a user intends to interact with one effector over another (e.g. a robotic arm vs. a computer cursor). This could lead to multi-effector systems where the user is able to seamlessly switch between end-effectors.
Alex will graduate in 2018 with an Sc.B. in Neuroscience.