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Auxiliary controller design and performance comparative analysis in closed-loop brain-machine interface system.

Hongguang Pan1,2, Haoqian Song3, Qi Zhang4

  • 1College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China. hongguangpan@163.com.

Biological Cybernetics
|October 4, 2021
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Summary
This summary is machine-generated.

This study compares auxiliary controllers for brain-machine interfaces (BMIs). Model predictive control (MPC) offers the highest accuracy but consumes more time, guiding BMI system design.

Keywords:
Auxiliary controller designBrain–machine interfaceClosed-loop systemPerformance analysis

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Area of Science:

  • Neuroscience
  • Control Engineering
  • Biomedical Engineering

Background:

  • Brain-machine interfaces (BMIs) enable brain-device interaction.
  • Electroencephalogram (EEG) signal variability limits BMI control accuracy.
  • Auxiliary controllers can enhance BMI performance, but their effectiveness varies.

Purpose of the Study:

  • To comprehensively compare and analyze the performance of different auxiliary controllers for BMI systems.
  • To provide a theoretical basis for selecting optimal auxiliary controllers in BMI design.
  • To evaluate controller trade-offs between accuracy and computational cost.

Main Methods:

  • Designed four auxiliary controllers: Simultaneous Perturbation Stochastic Approximation-Function Approximator (SPSA-FA), Iterative Feedback Tuning-PID (IFT-PID), Model Predictive Control (MPC), and Model-Free Control (MFC).
  • Constructed closed-loop BMI systems using an improved single-joint information transmission model and a decoder-based Wiener filter.
  • Compared controller performance in dynamic motion restoration tasks within the closed-loop BMI systems.

Main Results:

  • Tracking accuracy order: MPC > IFT-PID > SPSA-FA > MFC.
  • Time consumption order is the inverse of tracking accuracy.
  • Model Predictive Control (MPC) demonstrated superior tracking accuracy.
  • Model-Free Control (MFC) required the least time but had lower accuracy.

Conclusions:

  • Controller performance varies significantly, impacting BMI accuracy and speed.
  • Higher control accuracy is achieved at the cost of increased computation time.
  • Selection of an auxiliary controller should align with specific BMI application requirements and constraints.