Automatic control of reactive brain computer interfaces

Research output: Contribution to journalArticlepeer-review


This article discusses theoretical and practical aspects of real-time brain computer interface control methods based on Bayesian statistics. The theoretical aspects include how the data from the brain computer interface can be translated into a Gaussian mixture model that is used in the Bayesian statistics-based control methods. The practical aspects include how the control methods improve the performance of the brain computer interface. We use a reactive brain computer interface based on a visual oddball paradigm for the investigation and improvement of the performance of automatic control and feedback algorithms used in the system. By using automatic control for selection of the stimuli for the visual oddball experiment, the target stimulus is identified faster than if no automatic control is used. Finally, we introduce transfer learning using Gaussian mixture models, enabling a ready-to-use setup.
Original languageEnglish
Number of pages14
JournalIFAC Journal of Systems and Control
Issue numberBMS
Publication statusPublished - 2024 Mar 4

Subject classification (UKÄ)

  • Control Engineering

Free keywords

  • Brain computer interface
  • Automatic control
  • Gaussian mixture model
  • Bayesian statistics
  • Transfer learning
  • Monte Carlo simulation


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