Brain computer interfaces
Plug-and-play 2D cursor control
In my research at UCSF, I used electrocorticography (ECoG) based BCIs for motor neuroprostheses. ECoG places electrodes directly on the brain tissue and is known to be stable over time without degradation in signal quality. In our first project, to achieve 2D cursor control, we designed a Kalman Filter (KF) that was co-adaptive. Not only did the algorithm update its parameters to the user's brain activity, but it did so with a time-constant that in turn allowed the user to adapt their own brain activity and learn how to use the BCI. Our efforts resulted in a stable "plug-and-play" neural map of control, i.e., a decoder that did not require recalibration. We were able to then stack other control dimensions such as the ability to "point and click" (Silversmith*, Abiri*, Hardy*, Natraj* et. al, 2021).
Demo of the BCI system wherein the user (Bravo1) is controlling a 2D cursor and is able to successfully point and click at targets
high DoF BCI control of a robotic arm and hand
Our next project sought to expand neuroprosthetic control to higher degrees of freedom (hDoF) assistive devices such as the Kinova Jaco robot. Our goal was to allow long-term stable complex BCI control of reach-to-grasp and object manipulation capabilities. This is a high priority for patients with paralysis. Given the complexity of hDoF BCIs, co-adapative decoding algorithms - that involve the formation of a new BCI motor map - might necessitate long learning periods that can hinder real-world adoption. We wondered whether we could lessen the burden on the user, thereby allowing for rapid adoption.
My research uncovered principles of mesoscale representational stability and plasticity of simple motor commands - with neurofeedback - that eventually allowed for successful long-term neuroprosthetic control of a 2D cursor and a robotic arm and hand via a novel decoding algorithm (Natraj et. al., 2025, 2021). Importantly this decoding framework allows for within-session BCI control while leveraging across-session performance gains with plasticity. Accounting for representational drift eventually led to stable plug-and-play complex neuroprosthetic control.
