Encoder-Decoder Optimization for Brain-Computer Interfaces

Josh Merel, Donald M. Pianto, John P. Cunningham, Liam Paninski

Abstract: Author Summary Brain-computer interfaces are systems which allow a user to control a device in their environment via their neural activity. The system consists of hardware used to acquire signals from the brain of the user, algorithms to decode the signals, and some effector in the world that the user will be able to control, such as a cursor on a computer screen. When the user can see the effector under control, the system is closed-loop, such that the user can learn based on discrepancies between intended and actual kinematic outcomes. During training sessions where the user has specified objectives, the decoding algorithm can be updated as well based on discrepancies between what the user is supposed to be doing and what was decoded. When both the user and the decoding algorithm are simultaneously co-adapting, performance can improve. We propose a mathematical framework which contextualizes co-adaptation as a joint optimization of the user’s control scheme and the decoding algorithm, and we relate co-adaptation to optimal, fixed (non-adaptive) choices of decoder. We use simulation and human psychophysics experiments intended to model the BCI setting to demonstrate the utility of this approach.