Data for: Brain control of bimanual movement enabled by recurrent neural networks

Brain-computer interfaces have so far focused largely on enabling the
control of a single effector, for example a single computer cursor or
robotic arm. Restoring multi-effector motion could unlock greater
functionality for people with paralysis (e.g., bimanual movement).
However, it may prove challenging to decode the simultaneous motion of
multiple effectors, as we recently found that a compositional neural code
links movements across all limbs and that neural tuning changes
nonlinearly during dual-effector motion. In this study, we demonstrate the
feasibility of high-quality bimanual control of two cursors via neural
network (NN) decoders. This dataset represents all neural activity
recorded during these experiments. This includes the neural activity
corresponding to unimanual and bimanual hand movements during (1)
instructed delay experiments and (2) real-time BCI control of two
cursors.  Code associated with the data can be found here:
https://github.com/d-r-deo/bimanualBCI The journal article can be found
here: https://doi.org/10.1038/s41598-024-51617-3