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Updated: Aug 15, 2025

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
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Learning in a closed-loop brain-machine interface with distributed optogenetic cortical feedback.

Dorian Goueytes1, Henri Lassagne1, Daniel E Shulz1

  • 1Université Paris-Saclay, CNRS, Institut de Neurosciences Paris-Saclay, 91400 Saclay, France.

Journal of Neural Engineering
|December 29, 2022
PubMed
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Mice successfully used patterned optogenetic stimulation for prosthetic control via a brain-machine interface (BMI). This closed-loop feedback improved prosthesis joint control, demonstrating potential for advanced robotic limb applications.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Robotics

Background:

  • Brain-machine interfaces (BMIs) enable prosthesis control.
  • Cortical microstimulation can provide sensory feedback to prosthesis users.
  • Effective feedback is crucial for prosthesis control and embodiment.

Purpose of the Study:

  • To test a rotary optogenetic feedback system for encoding prosthetic joint movements.
  • To assess mice's ability to use this feedback in a closed-loop BMI task.
  • To investigate physiological constraints on decoding patterned cortical feedback.

Main Methods:

  • Mice controlled a virtual prosthesis joint's speed via motor cortex neuron activity.
  • Optogenetic stimulation on the somatosensory cortex provided continuous joint position feedback.
Keywords:
closed-loopelectrophysiologymotor brain-machine interfaceoptogenetics

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  • Performance was evaluated in a rewarded reaching task with and without feedback.

    • Main Results:

    • Mice improved prosthesis joint control using continuous cortical feedback.
    • Feedback enhanced reward detection, speed, and duration in the reward zone.
    • Control based on acceleration, not speed, did not improve motor performance.

    Conclusions:

    • Distributed cortical feedback can be effectively exploited for movement control in closed-loop BMIs.
    • Optimized feedback patterns and topology are key for successful integration.
    • Findings have direct applications for controlling rotary joints in robotic prostheses.