Symbiotic Brain-Machine Interfaces

1. Symbiotic Brain-Machine Interfaces Justin C. Sanchez, Ph.D. Assistant Professor Neuroprosthetics Research Group (NRG) University of Florida http://nrg.mbi.ufl.edu jcs77@ufl.edu

2. Develop direct neural interfaces to bypass injury. Communicate and control (closed-loop, real-time) directly via the interface. Enabling Neurotechnologies for Overcoming Paralysis Leuthardt

3. Vision for BMI in Daily Life Lebedev

4. What are the Building Blocks? Amplification Signal Sensing Pre-Processing Closed Loop BMI Telemetry Control Scheme Feedback Interpret Neural Activity Provide neurophysiologic basis and engineering theory for a fully implantable neural Interface for restoring communication and control

5. BMI lessons learned Relationship between user and BMI is inherently lopsided. Users are intelligent and can use dynamic brain organization and specialization while BMIs are passive devices that enact commands I/O models have difficulty contending with new environments without retraining Laboratory BMIs need to be better prepared for ADL

6. Translating Thoughts into Action: The Neural Code Neural System Stimulus Neural Response

7. Vision for Next Generation Brain-Machine Interaction • Intelligent behavior arises from the actions of an individual seeking to maximize received reward in a complex and changing world. • Perception-Action Cycle: Adaptive, continuous process of using sensory information to guide a series of goal-directed actions.

8. Co-Adaptive BMIs using Reinforcement Learning

9. Prerequesites for Symbiosis

10. Co-Adaptive BMI involves TWO intelligent agents involved in a continuous dialogue!!! COMPUTER AGENT actions rewards brain states environment ROBOT RAT’S BRAIN RAT’S BRAIN

11. Decoding using Reinforcement Learning • Rather than knowledge of the kinematic hand trajectory only a performance score is supplied. The score could represent reward or penalty, but does not directly provide information about how to correct for the error. • Reward based learning - try to choose strategy to maximize rewards. • RL originated from optimal control theory in Markov Decision Processes.

12. Incorrect Target Correct Target Starting Position Experimental Co-Adaptive BMI Paradigm Grid-space Robot Arm Map workspace to grid Rat Match LEDs • 27 discrete actions • 26 movements • 1 stationary Rat’s Perspective Match LEDs Water Reward Left Lever Right Lever

13. Agent - Value function estimation

14. Evidence for Symbiosis Overall Performance Valuation Change in Computer Agent Brain Reorganization

15. Key Concepts for the Future • Fully implantable interfaces are only half of the story. • Sharing of goals enables brain-computer dialogue and symbiosis • Need for intelligent decoders that assist and co-adapt with the user.

16. History of Man-Machine Interaction • “Implanting tiny machines into the nerves of the heart would make us less human” • Today, over half a million pacemakers are implanted annually! • We are at the frontier for integrating machines with the nervous system to restore and enhance function. Nicolelis

17. Tremendous team effort! Jack DiGiovanna - BME Babak Mahmoudi - BME Jose Principe - ECE Jose Fortes - ECE This work is supported by NSF project No. CNS-0540304

18. Please visit the lab website for publications and additional information. Neuroprosthetics Research Group • http://nrg.mbi.ufl.edu