Active learning for efficient querying from a human oracle with noisy response in a language-model assisted brain computer interface

RSVP Keyboard™ is a non-invasive electroencephalography (EEG) based brain computer interface (BCI) for letter by letter typing. In this system a sequence of symbols is presented on a computer screen in rapid serial visual presentation scheme to query a user´s intent. EEG evidence and language model are used in conjunction for joint inference of the intended symbol. Usually repetition of sequences is necessary to achieve high confidence in the intended symbol selection. This repetition usually results in degradation in the speed of typing while compensating for accuracy. In this manuscript, we develop a mathematical framework for active sequence selection that would optimize the amount of evidence obtained from user and would improve both typing speed and accuracy simultaneously. Our analysis based on Monte-Carlo simulation shows that one can effectively improve both typing speed and accuracy by optimizing the sequence of queries to be asked from the BCI user.