The report in the NEJM on the research: https://www.nejm.org/doi/full/10.1056/NEJMoa2027540?query=featured_home https://www.nejm.org/doi/full/10.1056/NEJMoa202

The report in the NEJM on the research: https://www.nejm.org/doi/full/10.1056/NEJMoa2027540?query=featured_home https://www.nejm.org/doi/full/10.1056/NEJMoa2027540?query=fe... Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria METHODS 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. RESULTS We decoded sentences from the participant’s cortical activity in real time at a median rate of 15.2 words per minute, with a median word error rate of 25.6%. In post hoc analyses, we detected 98% of the attempts by the participant to produce individual words, and we classified words with 47.1% accuracy using cortical signals that were stable throughout the 81-week study period. CONCLUSIONS In a person with anarthria and spastic quadriparesis caused by a brain-stem stroke, words and sentences were decoded directly from cortical activity during attempted speech with the use of deep-learning models and a natural-language model. (Funded by Facebook and others; ClinicalTrials.gov number, NCT03698149. opens in new tab.)