Data from: Stabilizing brain-computer interfaces through alignment of latent dynamics

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.