Data for: A high-performance speech neuroprosthesis

Brain-computer interfaces (BCIs) can restore communication to people who
have lost the ability to move or speak. In this study, we demonstrated an
intracortical BCI that decodes attempted speaking movements from neural
activity in motor cortex and translates it to text in real-time, using a
recurrent neural network decoding approach. With this BCI, our study
participant, who can no longer speak intelligibly due to amyotrophic
lateral sclerosis, achieved a 9.1% word error rate on a 50-word vocabulary
and a 23.8% word error rate on a 125,000-word vocabulary.  This
dataset contains all of the neural activity recorded during these
experiments, consisting of 12,100 spoken sentences as well as instructed
delay experiments designed to investigate the neural representation of
orofacial movement and speech production. The data have also been
formatted for developing and evaluating machine learning decoding methods,
and we intend to host a decoding competition. To this end, the data also
contain files for reproducing our offline decoding results, including a
language model and an example RNN decoder.  Code associated with
the data can be found
here: https://github.com/fwillett/speechBCI.