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A real-time brain-machine interface combining motor target and trajectory intent using an optimal feedback control

Maryam M Shanechi1, Ziv M Williams, Gregory W Wornell

  • 1School of Electrical and Computer Engineering, Cornell University, Ithaca, New York, United States of America. shanechi@cornell.edu

Plos One
|April 18, 2013
PubMed
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This study introduces a novel two-stage brain-machine interface (BMI) that jointly estimates movement targets and trajectories. This advanced BMI design improves accuracy and mimics natural sensorimotor control for better prosthetic device operation.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Robotics

Background:

  • Real-time brain-machine interfaces (BMIs) typically focus on either movement trajectory or target intent estimation.
  • Natural movements integrate both target and trajectory information.
  • BMIs function as feedback control systems, with subjects adjusting neural activity based on sensory feedback.

Purpose of the Study:

  • To develop a novel real-time BMI that jointly estimates movement target and trajectory.
  • To implement an optimal feedback control design for enhanced BMI performance.
  • To more closely mimic the natural sensorimotor control system.

Main Methods:

  • A two-stage optimal feedback control design was developed for real-time BMI.
  • Stage 1: Target decoding from neural spiking activity before movement initiation.

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  • Stage 2: Trajectory decoding using the decoded target and peri-movement spiking activity with a recursive Bayesian decoder and point process modeling.

    • Main Results:

    • The two-stage BMI demonstrated higher accuracy than either stage alone.
    • Joint estimation allowed target prediction to compensate for trajectory errors and vice versa.
    • The optimal feedback control design produced smoother trajectories with reduced estimation error.
    • The two-stage decoder outperformed linear regression approaches in offline analyses.

    Conclusions:

    • A BMI design that jointly estimates target and trajectory offers significant advantages.
    • This integrated approach more accurately reflects the sensorimotor control system.
    • The developed BMI shows promise for more natural and effective prosthetic control.