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Updated: Jun 25, 2026

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
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A RAPID PROTOTYPING ENVIRONMENT FOR CLOSED-LOOP NEUROMODULATION USING THE BRAIN INTERCHANGE SYSTEM.

Amir Hossein Ayyoubi1, Behrang Fazli Besheli2, Frederik Lampert2

  • 1Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, USA.

Proceedings of the ... Design of Medical Devices Conference. Design of Medical Devices Conference
|June 24, 2026
PubMed
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This study introduces a modular neuromodulation system for real-time neural sensing and stimulation. The Brain Interchange (BIC) system enhances epilepsy research by enabling multi-site monitoring and personalized therapy delivery.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Medical Devices

Background:

  • Implantable closed-loop neuromodulation systems require real-time neural sensing, biomarker detection, and targeted stimulation for personalized therapy.
  • Current systems face limitations in sampling rate and channel count, hindering applications like epilepsy research that demand high-frequency biomarkers and multi-site monitoring.

Purpose of the Study:

  • To present a modular closed-loop neuromodulation environment for the CorTec Brain Interchange (BIC) system.
  • To enable real-time data streaming, event detection, and flexible stimulation control for advanced neuromodulation research.

Main Methods:

  • Development of a modular closed-loop neuromodulation environment integrating a Simulink-based processing pipeline, real-time communication, and a graphical user interface.
Keywords:
Brain InterchangeClosed-Loop NeuromodulationEpilepsy

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  • System performance evaluation including impedance measurement, latency assessment across three stimulation modes, and in-lab demonstration of closed-loop stimulation using electrocardiogram (ECG).

    • Main Results:

    • The Brain Interchange (BIC) system supports sensing and stimulation across 32 channels at 1 kHz, addressing limitations in current implantable systems.
    • The developed modular environment demonstrated successful real-time data streaming, event detection, and flexible stimulation control.
    • Latency was assessed across different stimulation modes, and closed-loop functionality was validated using ECG.

    Conclusions:

    • The modular closed-loop neuromodulation environment provides a flexible platform for developing and validating neuromodulation algorithms.
    • The system can be extended for advanced biomarker detection and multi-channel neural interfaces, supporting future clinical research in areas like epilepsy.