Brain-to-voice neuroprosthesis
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Marking a breakthrough in the field of brain-computer interfaces (BCIs), a team of researchers from UC Berkeley and UC San Francisco has unlocked a way to restore naturalistic speech for people with severe paralysis.
This work solves the long-standing challenge of latency in speech neuroprostheses, the time lag between when a subject attempts to speak and when sound is produced. Using recent advances in artificial intelligence-based modeling, the researchers developed a streaming method that synthesizes brain signals into audible speech in near-real time.
As reported today in Nature Neuroscience, this technology represents a critical step toward enabling communication for people who have lost the ability to speak. The study is supported by the National Institute on Deafness and Other Communication Disorders (NIDCD) of the National Institutes of Health.
“Our streaming approach brings the same rapid speech decoding capacity of devices like Alexa and Siri to neuroprostheses,” said Gopala Anumanchipalli, Robert E. and Beverly A. Brooks Assistant Professor of Electrical Engineering and Computer Sciences at UC Berkeley and co-principal investigator of the study. “Using a similar type of algorithm, we found that we could decode neural data and, for the first time, enable near-synchronous voice streaming. The result is more naturalistic, fluent speech synthesis.”
“This new technology has tremendous potential for improving quality of life for people living with severe paralysis affecting speech,” said UCSF neurosurgeon Edward Chang, senior co-principal investigator of the study. Chang leads a clinical trial at UCSF that aims to develop speech neuroprosthesis technology using high-density electrode arrays that record neural activity directly from the brain surface. “It is exciting that the latest AI advances are greatly accelerating BCIs for practical real-world use in the near future,” he said.
