  A class of brain computer interfaces (BCIs) employs noninvasive recordings of
electroencephalography (EEG) signals to enable users with severe speech and
motor impairments to interact with their environment and social network. For
example, EEG based BCIs for typing popularly utilize event related potentials
(ERPs) for inference. Presentation paradigm design in current ERP-based letter
by letter typing BCIs typically query the user with an arbitrary subset
characters. However, the typing accuracy and also typing speed can potentially
be enhanced with more informed subset selection and flash assignment. In this
manuscript, we introduce the active recursive Bayesian state estimation
(active-RBSE) framework for inference and sequence optimization. Prior to
presentation in each iteration, rather than showing a subset of randomly
selected characters, the developed framework optimally selects a subset based
on a query function. Selected queries are made adaptively specialized for users
during each intent detection. Through a simulation-based study, we assess the
effect of active-RBSE on the performance of a language-model assisted typing
BCI in terms of typing speed and accuracy. To provide a baseline for
comparison, we also utilize standard presentation paradigms namely, row and
column matrix presentation paradigm and also random rapid serial visual
presentation paradigms. The results show that utilization of active-RBSE can
enhance the online performance of the system, both in terms of typing accuracy
and speed.
