EFRI-M3C: Distributed Brain Dynamics in Human Motor Control

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Intellectual Merit: This project aims at combining cognitive and computational neuroscience, neuroengineering and system identification towards a transformative understanding of the way distributed brain dynamics interact with motor activity in humans. 3-D body and limbs movement kinematics, eye movements and electroencephalographic (EEG) spatiotemporal brain data will be recorded simultaneously during motor control and adaptation in healthy and Parkinson?s disease patients. In particular, altered and real world motor tasks will be simulated in 3-D immersive virtual reality technology with force feedback robots providing proprioceptive interaction and feedback. Cognitive, behavioral and kinematics data will constrain the design of large-scale computational models of motor control and adaptation based on known anatomy and physiology of the basal ganglia. Neuromorphic engineering will guide the design of mobile embedded computational systems for real-time emulation of the brain-body models and closed-loop sensory-motor control for Parkinson?s patients. We expect that the development of new machines for neuro-rehabilitation will result in a threefold synergetic interaction between engineering and neuroscience: human-machine interactions will transform the notion of movement control and provide new contexts to study embodied cognition that will benefit neuroscience; in turn, new knowledge in neuroscience and motor control will accelerate the development of adaptive machines for rehabilitation and/or enhancement. Finally, comprehensive and predictive mathematical models of motor control implemented in neuromorphic hardware are expected to lead to new intelligent neuroprosthetic tools.Broader Impact: Outcomes of this research will contribute to the system-level understanding of humanmachine interactions and motor learning and control in real world environments for humans, and will lead to the development of a new generation of wireless brain and body activity sensors and adaptive prosthetics devices. This will advance our knowledge of human-machine interactions, stimulate the engineering of new brain/body sensors and actuators, and have a direct influence in diverse areas where humans are coupled with machines, such as brain-machine interfaces, prosthetics and telemanipulation. We anticipate that the confluence of cognitive and computational neuroscience, control theory and wearable, unobtrusive bioinstrumentation will provide novel non-invasive approaches or the treatment and neuro-rehabilitation of Parkinson?s disease and will potentially transform our understanding of brain/body interactions. The project draws graduate and undergraduate students across divisions and in the NSF Temporal Dynamics of Learning Center (TDLC) and Institute of Neural Computation (INC) at UCSD participating in interdisciplinary engineering and neuroscience aspects of the design, optimization, and training of largescale neuromorphic systems and their human interfaces. Through outreach channels on campus supported by the TDLC and the NSF Research Experience for Undergraduates (REU), the program will actively pursue increased participation in research and education of the next generation of scientists and engineers.