Decoding intended speech with an intracortical brain-computer interface in a person with long-standing anarthria and locked-in syndrome

Intracortical brain-computer interfaces (iBCIs) for decoding intended speech have provided individuals with ALS and severe dysarthria an intuitive method for high-throughput communication. These advances have been demonstrated in individuals who are still able to vocalize and move speech articulators. Here, we decoded intended speech from an individual with long-standing anarthria, locked-in syndrome, and ventilator dependence due to advanced symptoms of ALS. We found that phonemes, words, and higher order language units could be decoded well above chance. While sentence decoding accuracy was below that of demonstrations in participants with dysarthria, we attained an extensive characterization of neural signals underlying speech in a person with locked-in syndrome and identify directions for future improvement. These include closed-loop speech imagery training and decoding linguistic (rather than phonemic) units from neural signals in middle precentral gyrus to augment decoding at the sentence level. These results demonstrate that usable speech decoding from motor cortex may be feasible in people with anarthria and ventilator dependence.