Encoder-Decoder Optimization for Brain-Computer Interfaces

Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.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.