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Stabilizing brain-computer interfaces through alignment of latent dynamics.

Brianna M Karpowicz1, Yahia H Ali1, Lahiru N Wimalasena1

  • 1Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.

Nature Communications
|May 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Nonlinear Manifold Alignment with Dynamics (NoMAD), a new method for stabilizing brain-computer interfaces. NoMAD improves decoding accuracy and stability over long periods without needing frequent recalibration.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Intracortical brain-computer interfaces (iBCIs) restore motor function but suffer from decoding instability.
  • Current iBCIs require frequent recalibration due to neural interface degradation.
  • Existing unsupervised methods for iBCI stabilization do not account for latent neural dynamics.

Purpose of the Study:

  • To develop a stable decoding method for iBCIs that accounts for neural dynamics.
  • To improve the long-term performance and reduce recalibration needs of iBCIs.
  • To leverage latent manifold structure and dynamics for robust brain-computer interface control.

Main Methods:

  • Developed Nonlinear Manifold Alignment with Dynamics (NoMAD) platform.
  • Utilized recurrent neural network models to capture neural dynamics.
  • Employed unsupervised distribution alignment to map nonstationary neural data to consistent dynamics.
  • Applied NoMAD to motor cortex data from monkeys performing motor tasks.

Main Results:

  • NoMAD enabled accurate behavioral decoding from iBCI data.
  • Demonstrated unparalleled decoding stability over weeks to months.
  • Eliminated the need for supervised recalibration.
  • Showcased the effectiveness of incorporating dynamics into unsupervised iBCI stabilization.

Conclusions:

  • NoMAD offers a significant advancement in stable iBCI decoding.
  • Accounting for neural dynamics is crucial for long-term iBCI performance.
  • This unsupervised approach holds promise for restoring motor function in individuals with paralysis.