We describe an approach to improving the design and development of Brain-Computer Interface (BCI) applications by simulating the error-prone characteristics and subjective feel of electroencephalogram (EEG), motor-imagery based BCIs. BCIs have the potential to enhance the quality of life of people who are severely disabled, but it is often timeconsuming to test and develop the systems. Simulation of BCI characteristics allows developers to rapidly test design options, and gain both subjective and quantitative insight into expected behaviour without using an EEG cap. A further motivation for the use of simulation is that 'impairing' a person without motor disabilities in a game with a disabled BCI user can create a level playing field and help carers empathise with BCI users. We demonstrate a use of the simulator in controlling a game of Brain Pong.

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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 twostate 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.

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