title: Encoder-decoder optimization for brain-computer interfaces.
abstract: 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.
authors.affiliation: Neurobiology and Behavior Program, Columbia University, New York, New York, United States of America.
authors.forename: Josh
authors.initials: J
authors.lastname: Merel
authors.affiliation: Statistics Department, Columbia University, New York, New York, United States of America; Statistics Department, University of Brasília, Brasília, Distrito Federal, Brazil.
authors.forename: Donald M
authors.initials: DM
authors.lastname: Pianto
authors.affiliation: Statistics Department, Columbia University, New York, New York, United States of America.
authors.forename: John P
authors.initials: JP
authors.lastname: Cunningham
authors.affiliation: Statistics Department, Columbia University, New York, New York, United States of America.
authors.forename: Liam
authors.initials: L
authors.lastname: Paninski
mesh_terms: D000465:Algorithms; D062207:Brain-Computer Interfaces; D019295:Computational Biology; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D058117:Neural Prostheses; D011601:Psychophysics; D012815:Signal Processing, Computer-Assisted