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Related Experiment Video

Updated: Jun 21, 2026

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Making brain-computer interfaces as reliable as muscles.

Jonathan R Wolpaw1

  • 1National Center for Adaptive Neurotechnologies, Albany Stratton VA Medical Center and State University of New York at Albany, 113 Holland Ave, Albany, NY 12208, United States of America.

Journal of Neural Engineering
|May 27, 2025
PubMed
Summary
This summary is machine-generated.

Brain-computer interfaces (BCIs) can be as reliable as natural muscle actions by focusing on neuroscience to develop skills, not just neural engineering. This involves creating synthetic heksors that emulate natural skill networks for improved BCI function.

Keywords:
alternative communicationbrain-machine interfacebrain–computer interfaceheksormotor skillsnegotiated equilibriumsynthetic heksor

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Area of Science:

  • Neuroscience
  • Neural Engineering
  • Biomedical Engineering

Background:

  • Brain-computer interfaces (BCIs) currently restore basic communication but lack the reliability of natural muscle-based actions.
  • Existing BCI research primarily focuses on neural engineering to improve brain signal measurement and analysis.
  • Neural engineering alone is insufficient to achieve the high reliability required for complex actions.

Purpose of the Study:

  • To shift BCI research focus from solely neural engineering to include neuroscience principles.
  • To develop BCI skills that emulate the reliability and adaptability of natural human skills.
  • To explore the concept of 'heksors' in understanding and improving BCI functionality.

Main Methods:

  • Conceptualizing natural skills as 'heksors'—neuronal networks that self-modify to maintain skill features.
  • Introducing 'synthetic heksors' for BCIs, integrating neurons, synapses, and software.
  • Analyzing the interaction and co-adaptation between synthetic heksors and the central nervous system.

Main Results:

  • Natural skills are produced by adaptable neuronal networks ('heksors') that maintain key features through negotiated equilibrium.
  • BCI-based skills can be produced by synthetic heksors, which require co-adaptation with the user's nervous system.
  • Synthetic heksors can leverage multimodal sensory feedback and focus on maintaining essential skill features.

Conclusions:

  • Integrating neuroscience principles with neural engineering is crucial for enhancing BCI reliability.
  • Synthetic heksors offer a framework for developing BCI skills that mimic natural motor control.
  • Future BCIs could achieve muscle-like reliability through a deeper understanding of skill acquisition and maintenance.