UCSF neurosurgeon Edward Chang has spent the last decade working on a technology that would allow people with paralysis to communicate even though they're incapable of speech on their own.
Now, Chang and his team has succeeded in decoding full words from the brain activity. "It shows strong promise to restore communication by tapping into the brain's natural speech machinery," he said.
The first patient in the trial of the study suffered a devastating brainstem stroke 15 years ago which left him paralyzed and unable to speak. Since his injury, he communicated by using a pointer attached to a baseball cap to poke at letters on a computer screen.
Chang surgically implanted a high-density electrode array over the patient's speech motor cortex. Then, he and neurology professor Karunesh Ganguly and colleagues recorded 22 hours of neural activity in the patient's brain over several months while the patient attempted to vocalize some words many times.
The data was fed into custom neural network models, a form of artificial intelligence, to distinguish and identify specific subtle patterns in the brain activity to detect speech and identify which word the patient was trying to say.
The UCSF team found that their system was able to decode words from brain waves of the patient at a rate of up to 18 words per minute with up to 93 percent accuracy.
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