Design and development of a high-performance intra-cortical speech BCI

Many neurological injuries and diseases such as brainstem stroke and Amyotrophic Lateral Sclerosis (ALS) result in severe speech impairment, drastically reducing quality of life. Recent progress in brain-computer interfaces (BCI) has allowed these individuals to communicate, but performance is still far lower than typical spoken conversation speeds. We will study the precise mechanisms underlying speech production by collecting and analyzing neural activity from many individual neurons in multiple brain regions in clinical-trial participants with ALS, using our understanding from these data to design new machine learning algorithms, and finally testing these speech BCI designs in closed-loop studies with our participants.

Project Details

Funding Type:

SIGF - Graduate Fellowship

Award Year:


Lead Researcher(s):

Team Members:

Krishna V Shenoy (Primary Advisor, Electrical Engineering)
Scott W Linderman (Co-Advisor, Statistics)