Chang Lab at UCSF, human brain, speech, brain-computer-interfaces, neurosurgery

Joined August 2020
48 Photos and videos
ChangLabUCSF retweeted
For more than a century, scientists thought that Broca's area coordinates the muscle movements required to speak. But new @UCSF research from @ChangLabUCSF identifies the key role that the middle precentral gyrus (mPrCG) plays in this process. @jessierliu l8r.it/ua64
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Rather than coding specific vocal-tract movements during attempted speech, activity at these shared electrodes could be decoded into words prior to attempted speech, supporting the emerging role of the middle precentral gyrus in speech-motor planning.
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This work wouldn’t have been possible without our two amazing participants, Bravo-1 and Bravo-3, their family and caregivers, and the support of our team!
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Our latest research on the neural basis of speech-motor sequencing is now published in @NatureHumBehav! Check out this high-level explainer video and read the full paper here: nature.com/articles/s41562-0…
New in @NatureHumBehav, @jessierliu, Lingyun Zhao, PhD & @ChangLabUCSF show that the middle precentral gyrus coordinates the muscle movements required to speak, challenging the longstanding view that Broca's area controls this process: nature.com/articles/s41562-0…
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Our latest work on the neural mechanism for stopping speech production is published! See a brief summary below and the original paper linked to the post at the bottom.
In natural conversations, people can stop speaking at any time. How? Using high-density electrocorticography, Zhao et al. find a distinct neural signal in the human premotor cortex that inhibits speech output to achieve abrupt stopping. @ChangLabUcsf nature.com/articles/s41562-0…
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Excited to share our work on developing a bilingual speech neuroprosthesis that decodes cortical activity into English and Spanish sentences in a person with paralysis. Out today in @natBME! nature.com/articles/s41551-0…

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We leveraged this finding to demonstrate transfer learning across languages. Data collected in a first language could significantly expedite training a decoder in the second language, saving time and effort for the user.
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Our models also showed stable performance without retraining for ~2 months and these results were achieved ~4 years after ECoG implantation. We hope these findings can be scaled to more patients in the near future!
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