Collaborative Research: CDI Type I: Optimal and predictive control of neural prostheses using intracortical Brain Machine Interfaces

Johns Hopkins University, Baltimore MD

Investigators

Abstract

The objective of this research is to develop an optimal predictive feedback control framework for neuroprostheses that are enabled by brain machine interfaces (BMIs). BMIs, broadly defined, are systems that interface the brain and a machine (computer) to sense neuronal activity and cognitive processes to restore impaired motor tasks in disabled subjects. The research focuses on: (1) development of a computational modeling framework for BMI-based neuroprostheses that can incorporate large data sets of intracortical neuronal measurements; (2) identification of natural and "surrogate" optimal feedback sensing paths in neuromotor prostheses; (3) development of computationally efficient predictive control algorithms that exploit models and feedback pathways in neuroprostheses; and (4) experimental evaluation through simulated neuroprosthetic multi-finger grasping. Current neuroprosthetic devices are largely "open-loop" and have limited performance due to their inability to incorporate multivariable feedback, sensory and interactive signals. This research addresses this limitation, striving to advance next-generation feedback-enabled neuroprosthetic devices. The proposed conceptual integration of optimal control theory with principles of neuroengineering and computational neuroscience provides a framework for analyzing the numerous feedback modalities intrinsically embedded in a complex interconnected system of neurons. For broader impacts, the research seeks to enable the transition of BMI-based neuroprostheses and assistive devices to stable extended use in human subjects suffering from peripheral neuropathies, spinal cord injuries, neuromuscular disorders, and amputations. The project will introduce a computational neuroscience paradigm in feedback control education while training biomedical engineers to directly employ tools from computational feedback control theory. A science outreach effort with Asa Packer Elementary School in Bethlehem and the middle school Summer Robotics Camp in Baltimore will motivate and excite young minds to think about solving technological "grand challenges" through demonstrations of brain functionality.