User-Adaptive Text Entry for Augmentative and Alternative Communication

Abstract: The viability of an Augmentative and Alternative Communication device often depends on its ability to adapt to an individual user's unique abilities. Though human input can be noisy, there is often structure to our errors. For example, keyboard keys adjacent to a target may be more likely to be pressed in error. Furthermore, there can be structure in the input message itself (e.g. u' is likely to followq'). In a previous work, `Recursive Bayesian Coding for BCIs' (IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016), a query strategy considers these structures to offer an error-adaptive single-character text entry scheme. However, constraining ourselves to single-character entry limits performance. A single user input may be able to resolve more uncertainty than the next character has. In this work, we extend the previous framework to incorporate multi-character querying similar to word completion. During simulated spelling, our method requires $20\%$ fewer queries compared to single-character querying with no accuracy penalty. Most significantly, we show that this multi-character querying scheme converges to the information theoretic capacity of the discrete, memoryless user input model.