Data from: Decoding intended speech with an intracortical brain-computer interface in a person with longstanding 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 longstanding 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 can attain an extensive characterization
of the neural signals underlying speech in a person with locked-in
syndrome and, through our results, identify several directions for future
improvement. These include closed-loop speech imagery training and
decoding linguistic (rather than phonemic) units from neural signals in
the middle precentral gyrus. Overall, these results demonstrate that
speech decoding from the motor cortex may be feasible in people with
anarthria and ventilator dependence. For individuals with longstanding
anarthria, a purely phoneme-based decoding approach may lack the accuracy
necessary to support independent use as a primary means of communication;
however, additional linguistic information embedded within neural signals
may provide a route to augment the performance of speech decoders.