Breakthrough Brain-Computer Interface Enables Near Real-Time Speech for Paralyzed Patients

UC Berkeley and UCSF researchers unveil a system that decodes neural signals into naturalistic speech, restoring communication for individuals with severe paralysis.

The new brain-computer interface (BCI) system reduces speech synthesis latency to under one second, enabling fluid, near real-time communication.
Tested on a stroke patient unable to speak for 18 years, the system successfully synthesized speech using her pre-injury voice, enhancing her sense of embodiment.
The technology uses neural signals from the motor cortex and a deep learning model to decode brain activity into audible speech with high accuracy.
Researchers demonstrated the system's adaptability, including its ability to generalize beyond training data and synthesize rare words like those in the NATO phonetic alphabet.
Future research aims to enhance expressivity by incorporating tone, pitch, and loudness, while exploring broader applications, including non-invasive brain-sensing technologies.