FlashTypeTM: A Responsive cVEP-based BCI Speller

Brain computer interfaces (BCIs) offer new augmentative and alternative communication (AAC) opportunities for individuals with severe speech and motor impairments. Among different brain activities visually evoked potentials (VEP) are the most effective in BCI design, in terms of accuracy and speed of designed systems, including keyboard applications. In this talk, we describe FlashTypeTM, a brain interface based on code-VEP (cVEP) that utilizes a language model informed keyboard layout with static and dynamic keys.

The proposed system allows the user to move a cursor on a keyboard layout to make a symbol selection with minimum expected number of steps per selection. The static portion of the keyboard can be optimized according to a 1-gram letter probability distribution model for English. This portion is supplemented by a row of dynamically adjusted suggested characters, and a row of dynamically adjusted predicted words, to which the user may navigate with ease, reducing the average time to complete a word. This dynamic adjustment uses a 6-gram letter model for English that is fused with all recent EEG evidence to obtain a posterior probability distribution over the alphabet and dictionary. Moreover, to increase the typing speed and decrease the number of wrong decisions, we investigated two probabilistic graphical models for Bayesian inference, which uses context information and available EEG evidence to obtain the posterior probability distribution over the decision space. The two models will be discussed and performance analyses will be presented.

Although FlashTypeTM uses a cursor-based hierarchical selection method, due to the high accuracy of dynamically adjusted predictions, users tend to make the majority of their selections from the adaptive rows, which significantly reduces average time to type a letter or word by requiring minimal cursor movement steps.