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Related Experiment Video

Updated: Jun 6, 2026

Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation
11:12

Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation

Published on: July 16, 2014

Demand driven deep brain stimulation: regimes and autoregressive hidden Markov implementation.

John-Stuart Brittain1, Penny Probert-Smith, Tipu Z Aziz

  • 1Centre of Excellence in Personalized Healthcare, Institute of Biomedical Engineering, Old Road Campus Research Building, University of Oxford, Headington, OX3 7DQ, UK. john-stuart.aziz@nds.ox.ac.uk

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces novel neurofeedback device strategies for deep brain stimulation, aiming to mitigate side effects and improve treatment for Parkinson

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Last Updated: Jun 6, 2026

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Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Neuroscience

Background:

  • Deep brain stimulation (DBS) is a common surgical treatment for neurological disorders like Parkinson's disease.
  • Current DBS limitations include stimulator habituation, surgical risks, battery replacement costs, and neuropsychiatric side effects.
  • There is a need for advanced neurofeedback devices to optimize DBS therapy.

Purpose of the Study:

  • To present two distinct stimulation delivery regimes for chronic and acute neurofeedback in Parkinson's disease.
  • To explore implementation strategies for neurofeedback devices, focusing on tremor prediction.
  • To analyze the performance of detecting motor actions versus tremor.

Main Methods:

  • Development of two distinct stimulation delivery regimes for neurofeedback.
  • Application of vector-autoregressive hidden Markov models for tremor prediction.
  • Comparative performance analysis of detecting simple motor actions versus tremor.

Main Results:

  • The study presents distinct stimulation regimes applicable to chronic and acute Parkinsonian symptoms.
  • Vector-autoregressive hidden Markov models show promise for tremor prediction in DBS.
  • Preliminary analysis compares the detection capabilities for motor actions and tremor.

Conclusions:

  • Novel neurofeedback strategies can potentially address limitations of current deep brain stimulation.
  • Advanced modeling techniques like hidden Markov models are crucial for precise tremor prediction.
  • Further research is warranted to refine neurofeedback devices for improved Parkinson's disease management.