  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 follow `q'). 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.
