People with significant disabilities who are clinically described as neurologically "locked-in" are typically unable to move or speak. These people cannot use commercially available augmentative and alternative communication (AAC) or speech output devices. Current brain-computer interface (BCI) systems utilizing P300 detection are still only available as research tools. People who use a BCI system are required to spend considerable time training to use the system and must be willing to accept a fairly slow communication rate. In order to address the overarching goal of transitioning P300 Speller systems from the lab into the clinic, a team of basic scientists, engineers, and clinicians has been assembled. Central to reducing training time, improving robustness, and improving the communication rate is efficient P300 detection. Thus, approaches for improving P300 detection through model-based and statistical modeling of the P300 response are being explored. Optimizing the training procedure and the visual interface are also keys to reducing training time and improving communication rates. Techniques for optimizing the P300 speller user interface to minimize the number of presentations necessary to convey a word or concept are also being considered. Both the arrangement of the characters on the screen and the stimulation sequence are being investigated to determine their effects on performance.