Fully Closed Loop Test Environment for Adaptive Implantable Neural Stimulators Using Computational Models

Scott Stanslaski, Hafsa Farooqi, David Escobar Sanabria, Theoden Ivan Netoff

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Implantable brain stimulation devices continue to be developed to treat and monitor brain conditions. As the complexity of these devices grows to include adaptive neuromodulation therapy, validating the operation and verifying the correctness of these systems becomes more complicated. The new complexities lie in the functioning of the device being dependent on the interaction with the patient and environmental factors such as noise and artifacts. Here, we present a hardware-in-the-loop (HIL) testing framework that employs computational models of pathological neural dynamics to test adaptive deep brain stimulation (DBS) devices prior to animal or human testing. A brain stimulation and recording electrode array is placed in the saline tank and connected to an adaptive neuromodulation system that measures and processes the synthetic signals and delivers stimulation back into the saline tank. A data acquisition system is used to detect the stimulation and provide feedback to the computational model in order to simulate the effects of stimulation on the neural dynamics. In this study, we used real-time computational models to emulate the dynamics of epileptic seizures observed in the anterior nucleus of the thalamus (ANT) in epilepsy patients and beta band (11-35 Hz) oscillations observed in the subthalamic nucleus (STN) of Parkinson's disease (PD) patients. These models simulated neuronal responses to electrical stimulation pulses and the saline tank tested hardware interactions between the detection algorithms and stimulation interference. We tested and validated the operation of adaptive DBS algorithms for seizure and beta band power suppression embedded in an implantable DBS system (Medtronic Summit RCþS). This study highlights the utility of the proposed hardware-in-the-loop framework to systematically test the adaptive DBS systems in the presence of system aggressors such as environmental noise and stimulation-induced electrical artifacts. This testing procedure can help ensure correctness and robustness of adaptive DBS devices prior to animal and human testing.

Original languageEnglish (US)
Article number034501
JournalJournal of Medical Devices, Transactions of the ASME
Issue number3
StatePublished - Sep 2022

Bibliographical note

Funding Information:
Figure 1 illustration completed by Xavier Studio. For Fig. 3, reuse permission has been obtained from Journal of Theoretical Biology. Mean-field modeling of the basal ganglia-thalamocortical system. II Dynamics of parkinsonian oscillations via Copyright Clearance Center RightsLink License Number 4985461135314 with Elsevier. Wallin Discovery Fund (No. 20-8505156). National Institutes of Health, National Institute of Neurological Disorders and Stroke (P50-NS098573, No. 075-0886; Funder ID: 10.13039/100000065).

Publisher Copyright:
© 2022 by ASME.


  • computational models
  • neural sensing
  • neural stimulation


Dive into the research topics of 'Fully Closed Loop Test Environment for Adaptive Implantable Neural Stimulators Using Computational Models'. Together they form a unique fingerprint.

Cite this