Data and code from: Human learning of noninvasive brain-computer interfaces via manifold geometry

Brain-computer interfaces (BCIs) promise to restore and enhance a wide
range of human capabilities. However, a barrier to the adoption of BCIs is
that learning to control them can be difficult and slow, with inconsistent
results across users. We hypothesized that human BCI learning could be
accelerated by leveraging the naturally occurring geometric structure of
brain activity, or its intrinsic manifold, extracted using a
data-diffusion process. We trained participants on a noninvasive BCI that
allowed them to gain real-time control of an avatar in a virtual reality
game by modulating functional magnetic resonance imaging (fMRI) activity
in brain regions that support spatial navigation. We then perturbed the
mapping between fMRI activity patterns and the movement of the avatar to
test our manifold hypothesis. When the new mapping relied on directions of
significant variance along the intrinsic manifold, participants succeeded
in regaining control of the BCI by aligning their brain activity within
the manifold. When the new mapping did not rely on the intrinsic manifold,
participants could not learn to control the avatar again. These findings
show that the manifold geometry of brain activity constrains human
learning of a complex cognitive task in higher-order brain regions.
Manifold geometry may be a critical ingredient for unlocking the potential
of future human neurotechnologies.