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Updated: Jun 29, 2025

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
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Precise and low-power closed-loop neuromodulation through algorithm-integrated circuit co-design.

Jie Yang1, Shiqi Zhao1, Junzhe Wang1

  • 1CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, China.

Frontiers in Neuroscience
|March 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-integrated circuit for closed-loop neuromodulation, significantly reducing false alarms and power consumption in seizure prediction devices.

Keywords:
ASICartificial intelligenceclosed-loopevent-drivenimplantable deviceslow-powerneuromodulation

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Area of Science:

  • Neuromodulation
  • Artificial Intelligence
  • Integrated Circuits

Background:

  • Implantable neuromodulation devices improve neurological disorder treatment but face limitations like overstimulation in open-loop systems.
  • Closed-loop systems offer adaptive stimulation but suffer from high false alarm rates and energy inefficiency.
  • Current technologies hinder the full potential of responsive neuromodulation for brain diseases.

Purpose of the Study:

  • To develop an artificial intelligence-integrated circuit co-design to address false alarms and energy consumption in closed-loop neuromodulation.
  • To demonstrate the effectiveness of this system using a closed-loop seizure prediction online demonstration.
  • To improve the precision and efficiency of implantable devices for neurological treatments.

Main Methods:

  • Utilized neural network search and quantization to develop two models: a binary neural network for sensitivity and a convolutional neural network for false alarm rejection.
  • Fabricated a dedicated low-power processor in 55nm technology to implement the neural network models.
  • Designed the application-specific integrated circuit (ASIC) with reconfigurable and event-driven processing features.

Main Results:

  • Achieved a low false alarm rate of 0.1/h for seizure prediction using the optimized neural network models.
  • The fabricated ASIC occupies a small silicon area (5mm²) and demonstrates low average power consumption (142μW).
  • The proposed solution significantly reduces both false alarm rates and power consumption compared to existing state-of-the-art systems.

Conclusions:

  • The AI-integrated circuit co-design effectively reduces false alarms and power consumption in closed-loop neuromodulation systems.
  • This advancement holds promise for more precise, efficient, and safer implantable devices for treating neurological disorders.
  • The developed system offers a significant improvement over current technologies for responsive brain stimulation.