Title: Adaptive Stimulus Selection in ERP-Based Brain-Computer Interfaces by Maximizing Expected Discrimination Gain,
Abstract: Brain-computer interfaces (BCIs) can provide an alternative means of
communication for individuals with severe neuromuscular limitations. The
P300-based BCI speller relies on eliciting and detecting transient
event-related potentials (ERPs) in electroencephalography (EEG) data, in
response to a user attending to rarely occurring target stimuli amongst a
series of non-target stimuli. However, in most P300 speller implementations,
the stimuli to be presented are randomly selected from a limited set of options
and stimulus selection and presentation are not optimized based on previous
user data. In this work, we propose a data-driven method for stimulus selection
based on the expected discrimination gain metric. The data-driven approach
selects stimuli based on previously observed stimulus responses, with the aim
of choosing a set of stimuli that will provide the most information about the
user's intended target character. Our approach incorporates knowledge of
physiological and system constraints imposed due to real-time BCI
implementation. Simulations were performed to compare our stimulus selection
approach to the row-column paradigm, the conventional stimulus selection method
for P300 spellers. Results from the simulations demonstrated that our adaptive
stimulus selection approach has the potential to significantly improve
performance from the conventional method: up to 34% improvement in accuracy and
43% reduction in the mean number of stimulus presentations required to spell a
character in a 72-character grid. In addition, our greedy approach to stimulus
selection provides the flexibility to accommodate design constraints.