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  • What are we going to do with this information? Bring together a novel BMI architecture.
  • Active assistant to the paralyzed patient. Interaction is not passive but grows through experience.
  • The hidden layer of the MLP is a projection space where the state signal can be segmented. 3 hidden PEs was chosen based on some offline analysis. From this we can use a temporal difference error based ONLY on the rewards and our own predictions. Again details can be discussed offline – otherwise it is too boring. Action 3 example

Brain Machine Interfaces for Motor Control: Building Adaptive ... Brain Machine Interfaces for Motor Control: Building Adaptive ... Presentation Transcript

  • Symbiotic Brain-Machine Interfaces Justin C. Sanchez, Ph.D. Assistant Professor Neuroprosthetics Research Group (NRG) University of Florida http://nrg.mbi.ufl.edu [email_address]
  • Enabling Neurotechnologies for Overcoming Paralysis
    • Develop direct neural interfaces to bypass injury. Communicate and control (closed-loop, real-time) directly via the interface.
  • Vision for BMI in Daily Life Lebedev
  • What are the Building Blocks? Signal Sensing Amplification Pre-Processing Telemetry Interpret Neural Activity Control Scheme Feedback Closed Loop BMI Provide neurophysiologic basis and engineering theory for a fully implantable neural Interface for restoring communication and control
  • 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
  • Translating Thoughts into Action: The Neural Code Stimulus Neural System Neural Response Stimulus Neural Response Coding Given To determine Decoding To determine Given
  • 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.
  • Co-Adaptive BMIs using Reinforcement Learning
  • Prerequesites for Symbiosis
  • Co-Adaptive BMI involves TWO intelligent agents involved in a continuous dialogue!!! ROBOT actions rewards brain states RAT’S BRAIN environment RAT’S BRAIN COMPUTER AGENT
  • 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.
  • Experimental Co-Adaptive BMI Paradigm Match LEDs Grid-space Match LEDs Rat’s Perspective Water Reward Map workspace to grid Rat Robot Arm Left Lever Right Lever
    • 27 discrete actions
      • 26 movements
      • 1 stationary
    Incorrect Target Correct Target Starting Position
  • Agent - Value function estimation
  • Evidence for Symbiosis Valuation Change in Computer Agent Brain Reorganization Overall Performance
  • 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.
  • 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.
  • Tremendous team effort! Jack DiGiovanna - BME Babak Mahmoudi - BME This work is supported by NSF project No. CNS-0540304 Jose Principe - ECE Jose Fortes - ECE
  • Please visit the lab website for publications and additional information.
    • Neuroprosthetics Research Group
    • http://nrg.mbi.ufl.edu