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"... A team of neuroscientists and engineers at the University of California San Francisco (UCSF) used deep-learning and natural-language models to decode the brain waves of a clinical trial participant with severe paralysis and anarthria as he attempted to produce words and sentences. The study, published in the New England Journal of Medicine, showed a promising median decoding rate of 15.2 words per minute (with a median word error rate of 25.6%). This is approximately three times faster than the computer-based typing interface that the participant normally relies on for communication.
“We think that an ideal speech neuroprosthesis would allow a paralysed user to voluntarily and autonomously engage the system to communicate and interact with their personal devices ..."
"... We implanted a subdural, high-density, multielectrode array over the area of the sensorimotor cortex that controls speech in a person with anarthria (the loss of the ability to articulate speech) and spastic quadriparesis caused by a brain-stem stroke. Over the course of 48 sessions, we recorded 22 hours of cortical activity while the participant attempted to say individual words from a vocabulary set of 50 words. We used deep-learning algorithms to create computational models for the detection and classification of words from patterns in the recorded cortical activity. We applied these computational models, as well as a natural-language model that yielded next-word probabilities given the preceding words in a sequence, to decode full sentences as the participant attempted to say them. ..."
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