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Automatic control of reactive brain computer interfaces

Tufvesson, Pex LU and Heskebeck, Frida LU orcid (2024) In IFAC Journal of Systems and Control 27(BMS).
Abstract
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Brain computer interface, Automatic control, Gaussian mixture model, Bayesian statistics, Transfer learning, Monte Carlo simulation
in
IFAC Journal of Systems and Control
volume
27
issue
BMS
pages
14 pages
publisher
Elsevier
external identifiers
  • scopus:85187649558
ISSN
2468-6018
DOI
10.1016/j.ifacsc.2024.100251
project
Optimizing the Next Generation Brain Computer Interfaces using Cloud Computing
language
English
LU publication?
yes
id
208fd481-ff43-4fee-815f-5350e34078aa
date added to LUP
2024-03-07 11:57:19
date last changed
2024-04-09 12:58:10
@article{208fd481-ff43-4fee-815f-5350e34078aa,
  abstract     = {{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.}},
  author       = {{Tufvesson, Pex and Heskebeck, Frida}},
  issn         = {{2468-6018}},
  keywords     = {{Brain computer interface; Automatic control; Gaussian mixture model; Bayesian statistics; Transfer learning; Monte Carlo simulation}},
  language     = {{eng}},
  month        = {{03}},
  number       = {{BMS}},
  publisher    = {{Elsevier}},
  series       = {{IFAC Journal of Systems and Control}},
  title        = {{Automatic control of reactive brain computer interfaces}},
  url          = {{http://dx.doi.org/10.1016/j.ifacsc.2024.100251}},
  doi          = {{10.1016/j.ifacsc.2024.100251}},
  volume       = {{27}},
  year         = {{2024}},
}