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This study introduces a hidden Markov model for unsupervised adaptation in brain-computer interfaces (BCIs). This method improves BCI performance by retraining the system with inferred user targets, overcoming key clinical translation barriers.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Intracortical brain-computer interfaces (iBCIs) require frequent recalibration due to neural activity drift.
  • This drift leads to performance degradation and usability issues for iBCI users.
  • Current recalibration methods face challenges in maintaining long-term performance.

Purpose of the Study:

  • To develop an unsupervised adaptation method for iBCIs.
  • To improve the robustness and longevity of iBCI performance.
  • To overcome a major barrier in the clinical translation of BCIs.

Main Methods:

  • Introduction of a hidden Markov model (HMM) for inferring user targets during iBCI use.
  • Retraining the iBCI system using inferred targets for unsupervised adaptation.
  • Comparison with distribution alignment methods in simulations and human user studies.

Main Results:

  • The HMM-based approach outperformed distribution alignment methods in long-term closed-loop simulations and human use.
  • The method demonstrated capability for long-term unsupervised recalibration on a five-year iBCI dataset.
  • In contrast, data-distribution-matching approaches showed accumulating errors over time.

Conclusions:

  • The proposed target inference recalibration method enables robust, long-term unsupervised adaptation in iBCIs.
  • Leveraging task structure significantly enhances decoder performance.
  • This approach addresses a critical limitation for the clinical translation of brain-computer interfaces.