Towards the optimal design of an assistive communication interface with neural input

Brain Computer Interfaces (BCIs) directly connect the brain to an external device. Since neural signals encoding movement in tetraplegic individuals may remain robust, BCIs can utilize this signal for external device control. By recording activity in the motor cortex, intracortically-based BCIs have converted this neural signal to perform useful and intended motor functions [1]. The BrainGate2 Neural Interface System (NIS) is an investigational device that has allowed individuals with physical disability to interact directly with a computer, enabling them to navigate the screen with a cursor, click, and type. Simulating neural cursor control is useful for comparing the potential user performance of different communication interfaces. These simulations can be used to improve neurally controlled communication interface design towards restoring reliable communication for individuals with tetraplegia.