Data from: High-performance brain-to-text communication via handwriting

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 handwriting
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, whose hand was paralyzed
from spinal cord injury, achieved typing speeds that exceed those of any
other BCI yet reported: 90 characters per minute at 94.1% raw accuracy
online, and >99% accuracy offline with a general-purpose
autocorrect. This dataset contains all of the neural activity recorded
during these experiments, consisting of 1,000 sentences (43,501
characters) over 10.7 hours. The neural activity was recorded with two
microleectrode arrays implanted in hand area of motor cortex (96
electrodes each). The dataset also contains all of the real-time outputs
of the handwriting BCI.