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Adaptive neuron-to-EMG decoder training for FES neuroprostheses.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Engineering

Background:

  • Brain-machine interface (BMI) neuroprosthetic systems can restore grasp in spinal cord injury (SCI) models.
  • Cortical recordings offer a high-dimensional control signal for functional electrical stimulation (FES).
  • Training EMG decoders typically requires peripheral muscle activity recordings, which are unavailable in paralyzed individuals.

Purpose of the Study:

  • To present a novel method for training an EMG decoder without direct muscle activity recordings.
  • To enable brain-controlled FES neuroprosthetics for restoring hand function in paralyzed individuals.

Main Methods:

  • Monkeys performed a 2D isometric wrist force task to control a cursor.
  • A generic muscle force-to-endpoint force model related target forces to optimal EMG patterns.
  • EMG decoders were trained using a gradient descent algorithm comparing predicted EMG to optimal patterns.

Main Results:

  • The method was tested both offline and online, quantifying force prediction accuracy and closed-loop cursor control.
  • Offline and online results were compared to direct force decoders, including an optimal decoder.
  • The approach successfully trained an adaptive EMG decoder using inferred EMG activity.

Conclusions:

  • This novel training approach for adaptive EMG decoders can advance brain-controlled FES neuroprostheses.
  • Clinical implementation could restore hand function, enabling paralyzed individuals to grasp objects with near-normal motor intent.
  • Individualized EMG-to-force models and data from multiple grasping tasks can enhance generalization.