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PLoS Comput Biol. 2015 Jun 1;11(6):e1004288. doi: 10.1371/journal.pcbi.1004288. eCollection 2015 Jun.

Encoder-decoder optimization for brain-computer interfaces.

Author information

1
Neurobiology and Behavior Program, Columbia University, New York, New York, United States of America.
2
Statistics Department, Columbia University, New York, New York, United States of America; Statistics Department, University of Brasília, Brasília, Distrito Federal, Brazil.
3
Statistics Department, Columbia University, New York, New York, United States of America.

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.

PMID:
26029919
PMCID:
PMC4451011
DOI:
10.1371/journal.pcbi.1004288
[Indexed for MEDLINE]
Free PMC Article

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