The achievement, which was developed in collaboration with the first participant of a clinical research trial, builds on more than a decade of effort by UCSF neurosurgeon Edward Chang, MD, to develop a technology that allows people with paralysis to communicate even if they are unable to speak on their own. The study appears July 15 in the New England Journal of Medicine.
Each year, thousands of people lose the ability to speak due to stroke, accident, or disease. With further development, the approach described in this study could one day enable these people to fully communicate.
To investigate the potential of this technology in patients with paralysis, Chang partnered with colleague Karunesh Ganguly, MD, PhD, an associate professor of neurology, to launch a study known as “BRAVO” (Brain-Computer Interface Restoration of Arm and Voice). The first participant in the trial is a man in his late 30s who suffered a devastating brainstem stroke more than 15 years ago that severely damaged the connection between his brain and his vocal tract and limbs. Since his injury, he has had extremely limited head, neck, and limb movements, and communicates by using a pointer attached to a baseball cap to poke letters on a screen.
The participant, who asked to be referred to as BRAVO1, worked with the researchers to create a 50-word vocabulary that Chang’s team could recognize from brain activity using advanced computer algorithms. The vocabulary – which includes words such as “water,” “family,” and “good” – was sufficient to create hundreds of sentences expressing concepts applicable to BRAVO1’s daily life.
For the study, Chang surgically implanted a high-density electrode array over BRAVO1’s speech motor cortex. After the participant’s full recovery, his team recorded 22 hours of neural activity in this brain region over 48 sessions and several months. In each session, BRAVO1 attempted to say each of the 50 vocabulary words many times while the electrodes recorded brain signals from his speech cortex.
To translate the patterns of recorded neural activity into specific intended words, the other two lead authors of the study, Sean Metzger, MS and Jessie Liu, BS, both of the UCSF-UC Berkeley Joint PhD Program in Bioengineering, used custom neural network models, which are forms of artificial intelligence. When the participant attempted to speak, these networks distinguished subtle patterns in brain activity to detect speech attempts and identify which words he was trying to say.
To test their approach, the team first presented BRAVO1 with short sentences constructed from the 50 vocabulary words and asked him to try saying them several times. As he made his attempts, the words were decoded from his brain activity, one by one, on a screen.
The team found that the system was able to decode words from brain activity at rate of up to 18 words per minute with up to 93 percent accuracy (75 percent median). Contributing to the success was a language model Moses applied that implemented an “auto-correct” function, similar to what is used by consumer texting and speech recognition software.
According to UCSF