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The Muscle Cuff Regenerative Peripheral Nerve Interface for the Amplification of Intact Peripheral Nerve Signals
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Recovering Motor Activation with Chronic Peripheral Nerve Computer Interface.

Thomas E Eggers1, Yazan M Dweiri2, Grant A McCallum1

  • 1Neural Engineering Center, Biomedical Engineering, Case Western Reserve University, Cleveland, USA.

Scientific Reports
|September 22, 2018
PubMed
Summary
This summary is machine-generated.

This study demonstrates chronic neural recordings using the Flat Interface Nerve Electrode (FINE) to predict muscle activation for amputee prosthetics. High correlations were achieved, paving the way for advanced neural interfaces.

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Rehabilitation Technology

Background:

  • Peripheral nerve interfaces are crucial for restoring function in amputee patients.
  • Chronic extraction of motor activation signals from the nervous system remains a significant challenge.

Purpose of the Study:

  • To investigate the efficacy of the Flat Interface Nerve Electrode (FINE) for chronic motor activation recovery.
  • To develop and validate a method for predicting muscle activation from peripheral nerve recordings.

Main Methods:

  • Implantation of FINEs onto canine sciatic nerves.
  • Simultaneous recording of neural signals and hamstring muscle electromyograms (EMG) during treadmill ambulation.
  • Application of a novel Bayesian algorithm to recover fascicular signals and compare them with EMG.

Main Results:

  • Neural recordings were free from muscle interference and crosstalk.
  • High correlation coefficients (0.84 ± 0.07 and 0.61 ± 0.12) were observed between extracted fascicle signals and corresponding muscle EMGs.
  • Information transfer rates of approximately 5 and 1 bit per second were achieved for motor predictions.

Conclusions:

  • The FINE, coupled with a Bayesian algorithm, can successfully recover and predict motor activation signals from peripheral nerves.
  • This approach shows promise for developing advanced neural prosthetics for amputees.
  • Chronic neural recording for motor control is a feasible objective.