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Related Concept Videos

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Related Experiment Video

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An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
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Dareplane: a modular open-source software platform for BCI research with application in closed-loop deep brain

Matthias Dold1,2,3,4, Joana Pereira1,4,5, Bastian Sajonz4

  • 1Data-Driven Neurotechnology Lab, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.

Journal of Neural Engineering
|February 27, 2025
PubMed
Summary
This summary is machine-generated.

Dareplane, an open-source software platform, simplifies complex adaptive deep brain stimulation (aDBS) and brain-computer interface (BCI) research setups. It demonstrates technical feasibility and sufficient performance for real-world neurotechnology applications.

Keywords:
brain–computer interfacebrain–machine interfacec-VEPclosed-loopdeep brain stimulationelectrophysiologyopen source software

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computer Science

Background:

  • Adaptive deep brain stimulation (aDBS) research is hindered by complex experimental setups.
  • Developing robust and adaptable brain-computer interfaces (BCIs) requires flexible research platforms.

Purpose of the Study:

  • Introduce Dareplane, a modular, technology-agnostic, open-source software platform for BCI research.
  • Address the complexity challenges in setting up aDBS experiments.
  • Facilitate resilient and replicable neurotechnological system research.

Main Methods:

  • Developed Dareplane using a Python-based orchestration module for experimental setup.
  • Evaluated platform performance in three benchtop experiments with varying hardware (Arduino, implantable pulse generator, certified neurostimulator).
  • Demonstrated technical feasibility in a closed-loop aDBS session with a Parkinson's patient and a non-invasive BCI speller (c-VEP).

Main Results:

  • Dareplane is implemented and publicly available on GitHub.
  • Benchtop tests confirmed the platform's performance meets aDBS latency requirements.
  • Successful implementation of a timing-critical c-VEP speller achieved expected information transfer rates.

Conclusions:

  • Dareplane provides a modular, adaptable, and user-friendly solution for BCI and aDBS research.
  • The platform enhances the resilience and replicability of neurotechnology experimental setups.
  • Dareplane supports advancements in adaptive deep brain stimulation and brain-computer interface research.