Data from: Using adversarial networks to extend brain computer interface decoding accuracy over time

Existing intracortical brain computer interfaces (iBCIs) transform neural
activity into control signals capable of restoring movement to persons
with paralysis. However, the accuracy of the “decoder” at the heart of the
iBCI typically degrades over time due to turnover of recorded neurons. To
compensate, decoders can be recalibrated, but this requires the user to
spend extra time and effort to provide the necessary data, then learn the
new dynamics. As the recorded neurons change, one can think of the
underlying movement intent signal being expressed in changing coordinates.
If a mapping can be computed between the different coordinate systems, it
may be possible to stabilize the original decoder’s mapping from brain to
behavior without recalibration. We previously proposed a method based on
Generalized Adversarial Networks (GANs), called “Adversarial Domain
Adaptation Network” (ADAN), which aligns the distributions of latent
signals within underlying low-dimensional neural manifolds. However, we
tested ADAN on only a very limited dataset. Here we propose a method based
on Cycle-Consistent Adversarial Networks (Cycle-GAN), which aligns the
distributions of the full-dimensional neural recordings. We tested both
Cycle-GAN and ADAN on data from multiple monkeys and behaviors and
compared them to a third, quite different method based on Procrustes
alignment of axes provided by factor analysis. All three methods are
unsupervised and require little data, making them practical in real life.
Overall, Cycle-GAN had the best performance and was easier to train and
more robust than ADAN, making it ideal for stabilizing iBCI systems over
time.