Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering
Fig 1
Adaptive OFC-PPF BMI architecture.
(A) Monkey performing the self-paced delayed center-out movement task in brain control. The subject’s arms were confined within a primate chair in brain control. (B) Timeline of the center-out task (see Experimental Procedures for details). (C) Adaptive OFC-PPF converts the spiking activity into a discrete time-series of 0’s and 1’s by binning the spikes in small intervals containing at most one spike; this binary time-series is modeled as a point process. We thus perform the decoding and parameter adaptation with every binary spike event. (D) Adaptive OFC-PPF architecture. The architecture models the brain in closed-loop BMI control as an infinite-horizon optimal feedback-controller to infer its intended velocity during adaptation. The inputs to the infinite-horizon optimal feedback-controller model are the visual feedback of the decoded cursor kinematics (that the monkey observes) and the instructed target position (i.e., task goal). The inferred intended velocity is input to a point process filter for each neuron, which estimates the neuron’s parameters with every 0 and 1 spike event. These estimated parameters are used in the kinematic PPF decoder that decodes the kinematics with every 0 and 1 spike event. Initially, the architecture can provide assisted training to the subject by decoding the kinematics using a target-directed PPF decoder (see Methods). After assisted training is complete, a random-walk PPF is used to decode the kinematics. Once performance converges, adaptation stops and the trained random-walk PPF is used by the monkey to perform various BMI tasks, such as the center-out or the target-jump tasks.