  Brain-machine interfaces (BMIs) help the disabled restore body functions by
translating neural activity into digital commands to control external devices.
Neural adaptation, where the brain signals change in response to external
stimuli or movements, plays an important role in BMIs. When subjects purely use
neural activity to brain-control a prosthesis, some neurons will actively
explore a new tuning property to accomplish the movement task. The prediction
of this neural tuning property can help subjects adapt more efficiently to
brain control and maintain good decoding performance. Existing prediction
methods track the slow change of the tuning property in the manual control,
which is not suitable for the fast neural adaptation in brain control. In order
to identify the active neurons in brain control and track their tuning property
changes, we propose a globally adaptive point process method (GaPP) to estimate
the neural modulation state from spike trains, decompose the states into the
hyper preferred direction and reconstruct the kinematics in a dual-model
framework. We implement the method on real data from rats performing a
two-lever discrimination task under manual control and brain control. The
results show our method successfully predicts the neural modulation state and
identifies the neurons that become active in brain control. Compared to
existing methods, ours tracks the fast changes of the hyper preferred direction
from manual control to brain control more accurately and efficiently and
reconstructs the kinematics better and faster.
