ECoG data analyses to inform closed-loop BCI experiments for speech-based prosthetic applications.

Abstract

Brain Computer Interfaces (BCIs) assist individuals with motor disabilities by enabling them to control prosthetic devices with their neural activity. Performance of closed-loop BCI systems can be improved by using design strategies that leverage structured and task-relevant neural activity. We use data from high density electrocorticography (ECoG) grids implanted in three subjects to study sensory-motor activity during an instructed speech task in which the subjects vocalized three cardinal vowel phonemes. We show how our findings relate to the current understanding of speech physiology and functional organization of human sensory-motor cortex. We investigate the effect of behavioral variations on parameters and performance of the decoding model. Our analyses suggest experimental design strategies that may be critical for speech-based BCI performance.