Intracortical brain-computer interfaces (iBCIs) can help to restore movement and communication to people with chronic tetraplegia by recording neural activity from the motor cortex and translating it into the motion of an external device (typically a computer cursor or robotic arm). In this work, we focus on three avenues for advancement: (1) better understanding the feedback control loop created by the interaction between the user and the iBCI, (2) leveraging that understanding to improve the performance of decoding algorithms that translate neural activity into movement, and (3) restoring control over a person's own arm and hand by using a combined iBCI and muscle stimulation system.
In Chapters 2-3, we use data from the BrainGate2 pilot clinical trial to develop a feedback control model that describes how users modulate their neural activity to move towards their target, stop accurately, and correct for movement errors when using a linear decoder. We characterize the decoding errors we observed and show how they cause iBCI movements to differ from able-bodied movements. In Chapters 4-6, we explore three avenues for improving decoder performance based on our findings from Chapters 2-3. First, we improve the standard linear decoder by adding a separate decoding pathway that can extract non-linear movement scale information from the neural activity. Second, we show that our feedback control model can be used to optimize decoder performance by predicting which parameters will lead to the best closed-loop performance. Third, we test whether our feedback control model can improve decoder calibration by more accurately estimating the user's intended movements.
In Chapters 7-8, we make progress towards a combined iBCI and functional electrical stimulation (FES) system that can restore motion to a person's own arm and hand. In a non-human primate model, we develop and test a new decoding method that enables direct cortical control over muscle stimulation and that can be calibrated automatically in a clinically feasible way. Finally, we demonstrate a person in the BrainGate2 pilot clinical trial using a combined FES + iBCI system to make continuously controlled, multi-joint reaching and grasping movements to match target postures and to complete functional tasks.