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Probabilistic Simulation Framework for EEG-Based BCI Design.

Umut Orhan1, Hooman Nezamfar2, Murat Akcakaya3

  • 1Honeywell International Inc.

Brain Computer Interfaces (Abingdon, England)
|December 19, 2017
PubMed
Summary
This summary is machine-generated.

A new simulation framework for brain-computer interface (BCI) design using electroencephalography (EEG) reduces experimental burden. Monte Carlo simulations approximate real-time system performance, aiding designers in making informed choices.

Keywords:
Brain computer interfaceselectroencephalographyevent related potentialssimulationsteady state visually evoked potentials

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computer Science

Background:

  • Brain-computer interface (BCI) systems require extensive experimental sessions.
  • A simulation framework can aid in the initial design and evaluation of BCI systems.
  • Electroencephalography (EEG) is a common modality for BCI development.

Purpose of the Study:

  • To develop a Monte Carlo based probabilistic simulation framework for EEG-BCI design.
  • To provide a tool for designers to make informed design choices.
  • To assess the accuracy of the simulation framework by comparing it with real-time experiments.

Main Methods:

  • Development of a Monte Carlo based probabilistic simulation framework.
  • Utilizing event-related potential (ERP) based typing and steady-state evoked potential (SSVEP) based control interfaces as testbeds.
  • Comparison of simulation results with real-time experimental data.

Main Results:

  • The simulation framework generally provides a good approximation of real-time system performance.
  • Monte Carlo simulations demonstrate statistical trends in performance estimation.
  • Potential for over and underestimation of performance exists, but the framework offers valuable insights.

Conclusions:

  • The developed simulation framework is a useful tool for EEG-BCI design.
  • It can decrease the burden of experimental sessions for BCI users.
  • The framework aids designers in making crucial design decisions by approximating system performance.