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Feedback control policies employed by people using intracortical brain-computer interfaces.

Francis R Willett1,2, Chethan Pandarinath3,4,5, Beata Jarosiewicz6,7,8

  • 1Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.

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Summary

Researchers identified new feedback control policies in intracortical brain-computer interface (iBCI) users. Understanding these neural modulation rules can improve brain-computer interface decoder design.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Intracortical brain-computer interfaces (iBCIs) enable users to control external devices by modulating neural population activity.
  • Understanding the feedback control policies governing this neural modulation is crucial for enhancing iBCI performance.
  • Previous models of control policies may not fully capture user behavior in complex tasks.

Purpose of the Study:

  • To characterize the feedback control policies used by individuals operating an iBCI for cursor control.
  • To investigate whether users can adapt their control policies in response to changes in decoder dynamics.
  • To develop a more comprehensive model of neural control in iBCI applications.

Main Methods:

  • Studied three participants in the BrainGate2 clinical trial performing a 2D target acquisition task with an iBCI.
  • Utilized a velocity decoder with exponential smoothing and offline analysis to model neural activity based on cursor and target states.
  • Tested policy adaptation by manipulating decoder gain and temporal smoothing parameters.

Main Results:

  • Developed a novel model of user feedback control policies, accounting for gradual and sharp neural activity changes relative to target proximity.
  • Observed continuous feedback corrections even upon reaching the target and active velocity compensation to prevent overshooting.
  • Demonstrated user adaptation by showing attenuation of neural modulation with high cursor gain and increased damping with high smoothing dynamics.

Conclusions:

  • The proposed control policy model offers insights into neural activity variations during active iBCI control.
  • This model can inform the design of improved neural decoders for higher-performing iBCIs.
  • The findings facilitate better simulations of closed-loop iBCI movements and advance our understanding of neural control strategies.