Feasibility of Automatic Error Detect-and-Undo System in Human Intracortical Brain-Computer Interfaces
We investigated a new approach for improving iBCI performance which takes advantage of the closed loop nature of BCIs: users perceive the errors, and this shows up in their neural activity. Using offline analyses, we show the feasibility of automatically detecting and undoing errors from neural activity alone.
A High-Performance Neural Prosthesis Incorporating Discrete State Selection with Hidden Markov Models
Communication neural prostheses aim to restore the ability to efficient communication to people with paralysis and ALS. These systems record neural signals from the brain and translate them, through a decoder algorithm, into control signals for moving an end effector. In our study, monkeys controlled computer cursors to acquire targets on a keyboard-like grid.