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 <75%. We outline general principles based on separating channel, line and source coding in human-machine loop design. We develop a novel selection mechanism which can achieve arbitrarily reliable selection with a noisy two-state button. We show automatic online adaptation to changing channel statistics, and operation without precise calibration of error rates. A range of visualisations are used to construct user interfaces which implicitly code for these channels in a way that it is transparent to users. We validate our approach with a set of Monte Carlo simulations, and empirical results from a human-in-the-loop experiment showing the approach operates effectively at 50-70% of the theoretical optimum across a range of channel conditions.
Highlights
Most mainstream devices used for human input are reliable; for example, keyboard typing has a typical error rate of around 6-7% [1]
Inspired by our frustration at making electroencephalogram (EEG)-based brain-computer interfaces (BCIs) usable with standard interactions, we focus on the channel coding problem for very noisy binary inputs
We have presented a widely-applicable interface for 1-of-n selection for marginal reliability inputs with high-reliability displays
Summary
Most mainstream devices used for human input are reliable; for example, keyboard typing has a typical error rate of around 6-7% [1]. This has led to interaction models which apply occasional corrective steps, such as backspace, to resolve infrequent errors. There are marginal reliability input devices, in assistive technology, where errors are sufficiently frequent that this approach fails catastrophically. The classic example is a BCI where error rates in even binary selection exceed 30% for some subjects [2]. The result is interfaces that are susceptible to unrecoverable correction cascades where attempts to rollback previous errors induce even more errors. In this paper we consider the problem of implementing efficient and transparent channel coding in human-machine control, encoding user intention robustly so it can be transferred
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