  Designing brain-computer interfaces (BCIs) that can be used in conjunction
with ongoing motor behavior requires an understanding of how neural activity
co-opted for brain control interacts with existing neural circuits. For
example, BCIs may be used to regain lost motor function after stroke. This
requires that neural activity controlling unaffected limbs is dissociated from
activity controlling the BCI. In this study we investigated how primary motor
cortex accomplishes simultaneous BCI control and motor control in a task that
explicitly required both activities to be driven from the same brain region
(i.e. a dual-control task). Single-unit activity was recorded from
intracortical, multi-electrode arrays while a non-human primate performed this
dual-control task. Compared to activity observed during naturalistic motor
control, we found that both units used to drive the BCI directly (control
units) and units that did not directly control the BCI (non-control units)
significantly changed their tuning to wrist torque. Using a measure of
effective connectivity, we observed that control units decrease their
connectivity. Through an analysis of variance we found that the intrinsic
variability of the control units has a significant effect on task proficiency.
When this variance is accounted for, motor cortical activity is flexible enough
to perform novel BCI tasks that require active decoupling of natural
associations to wrist motion. This study provides insight into the neural
activity that enables a dual-control brain-computer interface.

Neurons and Cognition