Accurate speech decoding requires high-resolution neural interfaces

Patients suffering from debilitating neurodegenerative diseases often lose the ability to communicate, detrimentally affecting their quality of life. One promising solution to restore communication is to decode signals directly from the brain to enable neural speech prostheses. However, decoding has been limited by coarse recordings, which inadequately capture the rich spatio-temporal structure of human brain signals. To resolve this limitation, we performed novel high-resolution, micro-electrocorticographic (uECoG) neural recordings during speech production. We obtained neural signals with 56x higher spatial resolution and 1.48x higher signal-to-noise ratio compared to standard invasive recordings. This increased signal quality nearly doubled decoding accuracy compared to standard intracranial signals. Accurate decoding was dependent on the high-spatial resolution of the neural interface. Non-linear decoding models designed to utilize enhanced spatio-temporal neural information produced better results than standard techniques. We show for the first time that micro-scale neural interfaces can enable high-quality speech decoding for neural speech prostheses. ### Competing Interest Statement The authors have declared no competing interest.