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On Calibration Algorithms for Real-Time Brain-Computer Interfaces

Heskebeck, Frida LU orcid (2023) In Research Reports TFRT-3281
Abstract
A Brain-Computer Interface (BCI) is a system that, in real-time, translates the user's brain activity into commands that can be used to control applications, such as moving a cursor on the screen. The translation is made possible by machine learning methods and other algorithms. The thesis focuses on EEG-based BCIs which are the most common type of BCIs due to EEG measurements being non-invasive, having good temporal resolution, and being suitable for many applications. As of today, one of the biggest challenges for BCIs is the so-called calibration, which is necessary for the BCI to translate the user's brain activity correctly. The need for calibration comes from the variability of the brain signals over time and between users.... (More)
A Brain-Computer Interface (BCI) is a system that, in real-time, translates the user's brain activity into commands that can be used to control applications, such as moving a cursor on the screen. The translation is made possible by machine learning methods and other algorithms. The thesis focuses on EEG-based BCIs which are the most common type of BCIs due to EEG measurements being non-invasive, having good temporal resolution, and being suitable for many applications. As of today, one of the biggest challenges for BCIs is the so-called calibration, which is necessary for the BCI to translate the user's brain activity correctly. The need for calibration comes from the variability of the brain signals over time and between users.


This thesis presents an extensive review of the state-of-the-art algorithms for BCIs, focusing on the calibration problem. Amongst the presented algorithms are methods for processing the EEG data, machine learning algorithms, and a brief introduction to transfer learning and Riemannian geometry. A more in-depth exploration of the so-called multi-armed bandits and Markov decision processes as possible methods to streamline the calibration procedure is presented, as well as a real-time framework for gathering and testing algorithms. Such a framework is crucial for testing new approaches for efficient calibration. (Less)
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author
supervisor
organization
publishing date
type
Thesis
publication status
published
subject
in
Research Reports TFRT-3281
publisher
Department of Automatic Control, Lund Institute of Technology, Lund University
ISSN
0280-5316
project
Realtime Individualization of Brain Computer Interfaces
Optimizing the Next Generation Brain Computer Interfaces using Cloud Computing
language
English
LU publication?
yes
id
0b53fef0-8adb-49d5-81be-08d7cd91a89a
date added to LUP
2024-01-10 10:53:27
date last changed
2024-01-12 03:13:24
@misc{0b53fef0-8adb-49d5-81be-08d7cd91a89a,
  abstract     = {{A Brain-Computer Interface (BCI) is a system that, in real-time, translates the user's brain activity into commands that can be used to control applications, such as moving a cursor on the screen. The translation is made possible by machine learning methods and other algorithms. The thesis focuses on EEG-based BCIs which are the most common type of BCIs due to EEG measurements being non-invasive, having good temporal resolution, and being suitable for many applications. As of today, one of the biggest challenges for BCIs is the so-called calibration, which is necessary for the BCI to translate the user's brain activity correctly. The need for calibration comes from the variability of the brain signals over time and between users. <br/><br/><br/>This thesis presents an extensive review of the state-of-the-art algorithms for BCIs, focusing on the calibration problem. Amongst the presented algorithms are methods for processing the EEG data, machine learning algorithms, and a brief introduction to transfer learning and Riemannian geometry. A more in-depth exploration of the so-called multi-armed bandits and Markov decision processes as possible methods to streamline the calibration procedure is presented, as well as a real-time framework for gathering and testing algorithms. Such a framework is crucial for testing new approaches for efficient calibration.}},
  author       = {{Heskebeck, Frida}},
  issn         = {{0280-5316}},
  language     = {{eng}},
  month        = {{10}},
  note         = {{Licentiate Thesis}},
  publisher    = {{Department of Automatic Control, Lund Institute of Technology, Lund University}},
  series       = {{Research Reports TFRT-3281}},
  title        = {{On Calibration Algorithms for Real-Time Brain-Computer Interfaces}},
  url          = {{https://lup.lub.lu.se/search/files/168851333/FridaHeskebeck_Lic.pdf}},
  year         = {{2023}},
}