Automatic control of reactive brain computer interfaces
(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:
https://lup.lub.lu.se/record/208fd481-ff43-4fee-815f-5350e34078aa
- author
- Tufvesson, Pex LU and Heskebeck, Frida LU
- organization
- publishing date
- 2024-03-04
- 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}}, }