DOI: 10.5061/dryad.q83bk3jtp
title: Data from: Stabilizing brain-computer interfaces through alignment of latent dynamics

description: Intracortical brain-computer interfaces (iBCIs) restore motor function to
 people with paralysis by translating brain activity into control signals
 for external devices. In current iBCIs, instabilities at the neural
 interface result in a degradation of decoding performance, which
 necessitates frequent supervised recalibration using new labeled data. One
 potential solution is to use the latent manifold structure that underlies
 neural population activity to facilitate a stable mapping between brain
 activity and behavior. Recent efforts using unsupervised approaches have
 improved iBCI stability using this principle; however, existing methods
 treat each time step as an independent sample and do not account for
 latent dynamics. Dynamics have been used to enable high performance
 prediction of movement intention, and may also help improve stabilization.
 Here, we present a platform for Nonlinear Manifold Alignment with Dynamics
 (NoMAD), which stabilizes iBCI decoding using recurrent neural network
 models of dynamics. NoMAD uses unsupervised distribution alignment to
 update the mapping of nonstationary neural data to a consistent set of
 neural dynamics, thereby providing stable input to the iBCI decoder. In
 applications to data from monkey motor cortex collected during motor
 tasks, NoMAD enables accurate behavioral decoding with unparalleled
 stability over weeks-to months-long timescales without any supervised
 recalibration.
description: Human Participant Details  Participant T11
 had two 96-channel intracortical microelectrode arrays (Blackrock
 Microsystems, Salt Lake City, Utah) placed chronically into the left
 precentral gyrus (PCG) as part of the BrainGate pilot clinical trial
 (www.ClinicalTrials.gov; Identifier: NCT00912041). Permission for this
 study was granted by the U.S. FDA (Investigational Device Exemption
 #G090003) and the IRBs of Massachusetts General Hospital, Providence VA
 Medical Center, and Brown University. The participant gave informed
 consent to the study and resulting publications. This data has been
 previously published in Rubin, et al., J Neurosci 2022.
 Human Data Acquisition  During recording,
 neural activity was recorded at 30kHz from 192 channels and processed
 using a custom signal processing system. After digital downsampling,
 threshold crossing events were extracted in real time using 20 ms
 time-steps.  Human iBCI Experimental
 Paradigm  The participant first completed a center-out
 calibration task to optimize a Kalman filter to be used for subsequent
 tasks. Kalman filter weights were obtained using the rapid calibration
 procedure described previously. The participant then rested for a 30
 minute period during which baseline neural activity was
 recorded. The participant then completed a number of
 blocks of a “Simon” memory matching task. During each trial, the
 researcher displayed a sequential illumination of one of four colored
 targets on the screen. Each illumination was 400 ms in duration and
 accompanied by a distinct tone. The participant was asked to imagine
 moving his right hand to drive the cursor from the center of the screen
 out to each target in the order presented. After dwelling on the correct
 target for 300 ms, the acquisition was deemed successful and the cursor
 was automatically recentered at the origin. If the participant completed
 the sequence of four target acquisitions successfully, a tone indicated
 success and the next trial’s sequence was presented. A failed trial
 occurred if the cursor was dwelled on an incorrect target or if the
 participant failed to move the cursor to any target within 5s.
 Each trial’s sequence always used all four polygons exactly once.
 Each block contained 16 trials, with one sequence provided per trial. In
 any given block, 12 of the 16 total trials were a fixed “target” sequence.
 The target sequence was different for each recording session but was
 consistent across blocks within a given session. The remaining 4 trials
 were a randomly chosen distractor sequence. The distractor sequences were
 made up on any of the other possible 23 four-target sequences. The
 participant was not made aware that there was a target sequence. Between
 each block the participant was given the opportunity to pause or take a
 short break if needed.  For purposes of this study,
 each individual movement in the sequence of 4 movements was considered
 separately. Only successful movements were considered, and any movement
 that was part of a failed trial was discarded. We defined the intended
 cursor position as the straight line distance from the center of the
 screen to the center of the target, which progresses linearly in time for
 500ms, and the velocity as a constant speed over this time
 period.
description: # Data from: Stabilizing brain-computer interfaces through alignment of
 latent dynamics ## Files and variables Each file is saved in Neurodata
 Without Borders (NWB) format with time series data stored as acquisition
 fields and trial information stored in the trials field.  ####
 Acquisitions * **binned_spikes**: threshold crossings aggregated in 20ms
 bins for 192 channels * **cursor_pos**: continuous cursor position in the
 x- and y-directions  * **target_pos**: the x- and y- positions of the
 centers of the cued target * **cursor_vel**: continuous cursor velocity in
 the x- and y- directions * **block_num**: the identity of the block that
 each data point was collected in during the research session *
 **cond_id**: for simplicity, the condition identity corresponding to the
 instructed target location (one condition per possible target) #### Trials
 In addition to start and stop times, each trial contains the following
 fields: * **cond_id**: condition identity for a given trial * **tgt_x**: x
 position of the center of the cued target * **tgt_y**: y position of the
 center of the cued target * **result**: 1 if the trial was part of a
 correctly repeated sequence, 0 otherwise * **is_calibration**: 1 if the
 trial belonged to a calibration block, 0 otherwise * **block_num**: the
 block number within the research session that the trial belonged to ##
 Code/software NWB files can be loaded using public packages, such as
 PyNWB: [https://pynwb.readthedocs.io/en/latest/](https://pynwb.readthedocs.io/en/latest/)  ## Human subjects data The participant gave informed consent to the study and resulting publications. Data is de-identified as no personally identifiable information is included, subject is referred to using a codified identifier, and fewer than three indirect identifiers are included.