Skip to main content
Dryad

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

Data files

Dec 28, 2023 version files 138.36 MB
Jan 02, 2024 version files 138.36 MB
Jan 18, 2024 version files 138.36 MB

Abstract

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