Efficient human-machine control with asymmetric marginal reliability input devices

John H Williamson, Melissa Quek, Iulia Popescu, Andrew Ramsay and Roderick Murray-Smith

PLOS ONE, 2020, vol. 15, issue 6, 1-56

Abstract: Input devices such as motor-imagery brain-computer interfaces (BCIs) are often unreliable. In theory, channel coding can be used in the human-machine loop to robustly encapsulate intention through noisy input devices but standard feedforward error correction codes cannot be practically applied. We present a practical and general probabilistic user interface for binary input devices with very high noise levels. Our approach allows any level of robustness to be achieved, regardless of noise level, where reliable feedback such as a visual display is available. In particular, we show efficient zooming interfaces based on feedback channel codes for two-class binary problems with noise levels characteristic of modalities such as motor-imagery based BCI, with accuracy

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0233603

DOI: 10.1371/journal.pone.0233603

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