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Brain-machine interfaces (BMIs) help the disabled restore body functions bytranslating neural activity into digital commands to control external devices.Neural adaptation, where the brain signals change in response to externalstimuli or movements, plays an important role in BMIs. When subjects purely useneural activity to brain-control a prosthesis, some neurons will activelyexplore a new tuning property to accomplish the movement task. The predictionof this neural tuning property can help subjects adapt more efficiently tobrain control and maintain good decoding performance. Existing predictionmethods 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 orderto identify the active neurons in brain control and track their tuning propertychanges, we propose a globally adaptive point process method (GaPP) to estimatethe neural modulation state from spike trains, decompose the states into thehyper preferred direction and reconstruct the kinematics in a dual-modelframework. We implement the method on real data from rats performing atwo-lever discrimination task under manual control and brain control. Theresults show our method successfully predicts the neural modulation state andidentifies the neurons that become active in brain control. Compared toexisting methods, ours tracks the fast changes of the hyper preferred directionfrom manual control to brain control more accurately and efficiently andreconstructs the kinematics better and faster.

Tracking Fast Neural Adaptation by Globally Adaptive Point Process Estimation for Brain-Machine Interface