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Peripheral neural interfaces for reading high-frequency brain signals.

Jaime Ibáñez1,2,3, Blanka Zicher4, Etienne Burdet4

  • 1BSICoS Group, Departamento de Ingeniería Electrónica y Comunicaciones, Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza, Zaragoza, Spain. jibanez@unizar.es.

Nature Biomedical Engineering
|June 27, 2025
PubMed
Summary
This summary is machine-generated.

Peripheral neural interfaces using muscle sensors offer a non-invasive method to decode central nervous system (CNS) activity by analyzing motor neuron (MN) outputs. This approach promises advancements in human-machine interfacing by estimating neural signals reaching MNs.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Machine Interfacing

Background:

  • Accurate central nervous system (CNS) recording is crucial for human-machine interfaces.
  • Current CNS technologies face limitations in resolution, interference sensitivity, and invasiveness.
  • Motor neurons (MNs) are the CNS output layer, controlling muscle activity.

Purpose of the Study:

  • To explore non-invasive peripheral neural interfaces for CNS activity estimation.
  • To investigate the potential of muscle sensors to decode CNS signals reaching MNs.
  • To assess the feasibility of MN-based CNS interfaces.

Main Methods:

  • Utilized muscle recordings and deep learning algorithms.
  • Decoded spiking activity of spinal motor neurons (MNs) in real time.
  • Estimated MN input signals from MN output activity.

Main Results:

  • Achieved real-time, high-accuracy decoding of spinal MN spiking activity from muscle recordings.
  • Demonstrated that MN output signals can be used to estimate MN input signals.
  • Identified peripheral neural interfaces as a promising non-invasive approach.

Conclusions:

  • Peripheral neural interfaces using muscle sensors can non-invasively estimate CNS activity reaching MNs.
  • This approach can capture neural signals not directly related to force production.
  • Further research and testing are needed to validate MN-based CNS interfaces in various settings.