DOI: 10.5061/dryad.sn02v6xbb
title: Data for: Brain control of bimanual movement enabled by recurrent neural networks

description: 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
description: Neural signals were recorded from two 96-channel Utah
 microelectrode arrays using the NeuroPort<sup>TM</sup> system
 from Blackrock Microsystems. First, neural signals were analog filtered
 from 0.3 to 7.5 kHz and subsequently digitized at 30kHz with 250 nV
 resolution. Next, common mode noise reduction was accomplished via a
 common average reference filter which subtracted the average signal across
 the array from every electrode. Finally, a digital high-pass filter at 250
 Hz was applied to each electrode prior to spike detection.
 Spike threshold crossing detection was implemented using a -3.5 x
 RMS threshold applied to each electrode, where RMS is the
 electrode-specific root mean square of the time series voltage recorded on
 that electrode. Neural data was recorded from
 participant T5 in 3-5 hour “sessions”, with breaks, on scheduled days. T5
 either performed attempted movements of one or both hands as governed by
 an instructed delay task, or performed real-time brain-computer interface
 control of two cursors.<br><br>This dataset is deidentified
 and contains binned spiking data (20ms bins) and task information, such as
 cursor positions, target positions, and trial timing.
description: # Data from: 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. Neural activity was recorded with microelectrode
 arrays, and neural features are provided in the form of binned threshold
 crossings (20 ms bins). This dataset represents all of the 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.  ## Description of the data and file structure There is one .zip
 file that contains the data for each of the 13 sessions included in the
 study. Each session's data is provided as its own .mat file and may
 contain the following: (1) data from the instructed delay tasks that
 assessed tuning to attempted unimanual and bimanual hand movements (these
 data are referred to as 'open-loop' or OL) (2) data from the
 real-time BCI control of two cursors (these data are referred to as
 'closed-loop' or CL, and occasionally as 'closed-loop
 recalibration' or CLR) All data consists of .mat files that are
 intended to be loaded with MATLAB or Python (scipy.io.loadmat). There is
 one readme file that describes the variables and experiments in detail. ##
 Sharing/Access information The data currently reside only on Dryad, and
 data were not derived from any other sources. ## Code/Software Code
 associated with the data can be found here: