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Design and testing of a 96-channel neural interface module for the Networked Neuroprosthesis system.

Autumn J Bullard1, Samuel R Nason1, Zachary T Irwin1

  • 11Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI USA.

Bioelectronic Medicine
|April 2, 2020
PubMed
Summary

This study introduces a new neural recording module for brain-machine interfaces, enabling fully implantable, intracortically-controlled functional electrical stimulation systems for spinal cord injury patients.

Keywords:
Brain machine interfaceFunctional electrical stimulationImplantableLow powerNeural interface

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Bioelectronic Medicine

Background:

  • Spinal cord injury (SCI) severely impacts motor function and quality of life.
  • Current functional electrical stimulation (FES) systems often require residual movements for control, limiting use in high-level SCI.
  • Existing brain-machine interfaces (BMI) for FES require transcutaneous leads and struggle with high signal bandwidth, hindering clinical application.

Purpose of the Study:

  • To develop a 96-channel neural recording module prototype compatible with the implantable Networked Neuroprosthesis (NNP) system.
  • To reduce power consumption by extracting power and limiting data transmission bandwidth.
  • To enable intracortical control of FES systems for improved SCI rehabilitation.

Main Methods:

  • Designed and tested a 1x4 cm, 96-channel neural recording module prototype.
  • Integrated the module with the NNP system specifications.
  • Reduced power requirements by extracting power between 0.3-1 kHz instead of transmitting high-bandwidth raw neural data.

Main Results:

  • The module successfully sampled 96 channels at approximately 2 kSps with a power consumption of 33.6 mW.
  • A strong correlation was found between average spiking band power and neural spike rate (R=0.8656 in Monkey N, R=0.8023 in Monkey W).
  • The module demonstrated successful communication over the NNP network within power constraints.

Conclusions:

  • The developed neural recording module meets the NNP system's power and bandwidth limitations.
  • This module can extend the NNP, paving the way for a clinically viable, fully implantable, intracortically-controlled FES system.
  • The advancement holds significant potential for the field of bioelectronic medicine and SCI treatment.