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An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
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Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine

Miri Benyamini1, Miriam Zacksenhouse1

  • 1Brain-computer Interfaces for Rehabilitation Laboratory, Department of Mechanical Engineering, Technion - Israel Institute of Technology Haifa, Israel.

Frontiers in Systems Neuroscience
|June 5, 2015
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Summary
This summary is machine-generated.

Brain-machine interfaces (BMIs) show altered neural modulations during use. Our model suggests increased process noise from imperfect BMI filters explains these changes in neural signals during brain control.

Keywords:
brain-machine interfacescomputational motor controlneural modulationsoptimal feedback controlprocess noise

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Robotics

Background:

  • Brain-machine interfaces (BMIs) reveal abrupt changes in neural modulations during operation and cessation of movement.
  • Neural modulations correlated with movement kinematics remain largely unchanged.

Purpose of the Study:

  • To demonstrate that simulated neurons, using an optimal feedback controller, can replicate observed neural modulation changes in BMI experiments.
  • To investigate the role of state estimation and control signals in neural activity during simulated BMI tasks.

Main Methods:

  • Simulated an optimal feedback controller for state estimation integrating visual, proprioceptive, and internal model feedback.
  • Modeled two neural populations encoding state or state and control signals, simulating neural recordings.
  • Generated spike counts using doubly stochastic Poisson processes with linear tuning curves.

Main Results:

  • The model successfully replicated kinematics and neural activity patterns during reaching movements.
  • Simulated neural activity mirrored observed changes in neural modulations when switching to brain control.
  • Increased process noise in simulations produced similar neural modulation patterns.

Conclusions:

  • Observed neural modulation changes in BMI experiments are likely due to increased process noise from imperfect BMI filters.
  • This noise leads to increased signal variance in state estimation and control signals.
  • The findings provide a computational explanation for neural adaptation in BMI users.