Encoder-decoder optimization for brain-computer interfaces

• Published in: PLoS computational biology Vol. 11; no. 6; p. e1004288
• Main Authors: Merel, Josh, Pianto, Donald M, Cunningham, John P, Paninski, Liam
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.06.2015; Public Library of Science (PLoS)
• Subjects: Algorithms; Brain-Computer Interfaces; Computational Biology; Computer Simulation; Experiments; Funding; Humans; Learning; Models, Neurological; Neural Prostheses; Optimization; Psychophysics; Signal Processing, Computer-Assisted
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1004288

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.