Automatic Control of Reactive Brain Computer Interfaces
Tufvesson, Pex LU and Heskebeck, Frida LU
(2023)
- Abstract
- This article discusses practical and theoretical aspects of real-time brain computer interface control methods based on Bayesian statistics. We investigate and improve the performance of automatic control and feedback algorithms of a reactive brain computer interface based on a visual oddball paradigm for faster statistical convergence. We introduce transfer learning using Gaussian mixture models, enabling a ready-to-use setup.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/71f728df-d61f-4b2c-9cc6-c8be683c75b6
- author
- Tufvesson, Pex
LU
and Heskebeck, Frida
LU
- organization
- publishing date
- 2023-10-11
- type
- Working paper/Preprint
- publication status
- published
- subject
- pages
- 32 pages
- publisher
- arXiv.org
- DOI
- 10.48550/arXiv.2310.07408
- project
- Optimizing the Next Generation Brain Computer Interfaces using Cloud Computing
- language
- English
- LU publication?
- yes
- id
- 71f728df-d61f-4b2c-9cc6-c8be683c75b6
- date added to LUP
- 2023-10-12 14:18:04
- date last changed
- 2024-03-08 07:40:01
@misc{71f728df-d61f-4b2c-9cc6-c8be683c75b6,
abstract = {{This article discusses practical and theoretical aspects of real-time brain computer interface control methods based on Bayesian statistics. We investigate and improve the performance of automatic control and feedback algorithms of a reactive brain computer interface based on a visual oddball paradigm for faster statistical convergence. We introduce transfer learning using Gaussian mixture models, enabling a ready-to-use setup.}},
author = {{Tufvesson, Pex and Heskebeck, Frida}},
language = {{eng}},
month = {{10}},
note = {{Preprint}},
publisher = {{arXiv.org}},
title = {{Automatic Control of Reactive Brain Computer Interfaces}},
url = {{http://dx.doi.org/10.48550/arXiv.2310.07408}},
doi = {{10.48550/arXiv.2310.07408}},
year = {{2023}},
}