FlashType<inline-formula> <tex-math notation="LaTeX">$^{\text{TM}}$</tex-math> </inline-formula>: A Context-Aware c-VEP-Based BCI Typing Interface Using EEG Signals

Abstract

Brain computer interfaces (BCIs) offer individuals with disabilities an alternative channel of communication and control, hence they have been receiving increasing interest. BCIs can also be useful for healthy individuals in situations limiting their movement or where other computer interaction modalities need to be supplemented. Event-related and steady state visually evoked potentials (SSVEPs) are the top two brain signal types used in developing BCIs that allow the user to make a choice from a discrete set of options, including the selection of commands from a menu for a robot or computer to perform, as well as typing letters, symbols, or icons for communication. Popular BCI speller paradigms, such as the P300 Matrix Speller, RSVP Keyboard $^{\rm TM}$ or SSVEP spellers in which the letters on the keyboard display flicker, are sensitive to the font, size and presentation speed. In addition, sensitivity to eye gaze control plays a significant role in usability of most of these keyboards. We present a code-VEP based BCI, utilized in a language model assisted keyboard application. Utilizing a cursor based selection method, stimuli and targets are separated. FlashType $^{\rm TM}$ separates visual stimulation from alphabet presentation to achieve performance invariance under presentation variations. Therefore, FlashType $^{\rm TM}$ can be used for all languages, including the ones containing symbols and icons. FlashType $^{\rm TM}$ , contains a Static Keyboard , a row of Suggested Characters and a row of Predicted Words . FlashType $^{\rm TM}$ , by default, uses only one EEG electrode and four stimuli. The system can operate using only one stimulus at a lower selection rate, useful for individuals with limited or no gaze control. This feature is to be explored in future. Replacing letters with text or icons representing commands would allow controlling a computer or robot. In this study, FlashType $^{\rm TM}$ has been evaluated by three individuals performing 10 Mastery tasks. In depth experimentation, such as assessing the system with potential end users writing long passages of text, will be done in future.