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A control-theoretic system identification framework and a real-time closed-loop clinical simulation testbed for

Yuxiao Yang1, Allison T Connolly1, Maryam M Shanechi1,2

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This study introduces a new framework for closed-loop electrical brain stimulation, using novel waveforms for system identification and a hardware-in-the-loop simulation testbed. This approach enables precise, real-time control for treating neurological and neuropsychiatric disorders.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Control Theory

Background:

  • Closed-loop electrical brain stimulation offers personalized treatment for neurological and neuropsychiatric disorders by adapting stimulation based on real-time neural activity.
  • Developing these systems requires accurate input-output (IO) dynamic models derived from data and a realistic simulation testbed for validation.

Purpose of the Study:

  • To develop a control-theoretic system identification framework for creating dynamic IO models of neural activity suitable for closed-loop control.
  • To create a realistic clinical hardware-in-the-loop (HIL) simulation testbed for designing and validating closed-loop controllers.

Main Methods:

  • A data-driven, linear state-space IO model was employed, characterizing neural activity via a low-dimensional hidden state.
  • Novel input waveforms, including binary noise (BN)-modulated and generalized BN (GBN)-modulated pulse trains, were designed for efficient and safe system identification.
  • A real-time HIL simulation testbed was developed using the [Formula: see text] device to incorporate realistic disturbances and artifacts.

Main Results:

  • The closed-loop controller, designed from IO models identified using the BN-modulated waveform, achieved precise control, comparable to a controller with perfect model knowledge.
  • The GBN-modulated waveform demonstrated improved performance under limited system identification time.

Conclusions:

  • The developed system identification framework and HIL simulation testbed are crucial for advancing model-based closed-loop electrical brain stimulation systems.
  • This work facilitates the development of more effective treatments for neurological and neuropsychiatric disorders.