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Enabling Bio-Feedback using Real-Time fMRI
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
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2008 (English)In: 47th IEEE Conference on Decision and Control, 2008, CDC 2008, IEEE , 2008, 3336-3341 p.Conference paper, Published paper (Refereed)
Abstract [en]

Despite the enormous complexity of the human mind, fMRI techniques are able to partially observe the state of a brain in action. In this paper we describe an experimental setup for real-time fMRI in a bio-feedback loop. One of the main challenges in the project is to reach a detection speed, accuracy and spatial resolution necessary to attain sufficient bandwidth of communication to close the bio-feedback loop. To this end we have banked on our previous work on real-time filtering for fMRI and system identification, which has been tailored for use in the experiment setup. In the experiments presented the system is trained to estimate where a person in the MRI scanner is looking from signals derived from the visual cortex only. We have been able to demonstrate that the user can induce an action and perform simple tasks with her mind sensed using real-time fMRI. The technique may have several clinical applications, for instance to allow paralyzed and "locked in" people to communicate with the outside world. In the meanwhile, the need for improved fMRI performance and brain state detection poses a challenge to the signal processing community. We also expect that the setup will serve as an invaluable tool for neuro science research in general.

Place, publisher, year, edition, pages
IEEE , 2008. 3336-3341 p.
Series
IEEE Conference on Decision and Control. Proceedings, ISSN 0191-2216
Keyword [en]
fMRI, System identification, Bio-feedback
National Category
Engineering and Technology Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-44641DOI: 10.1109/CDC.2008.4738759ISI: 000307311603077Local ID: 77222ISBN: 978-1-4244-3123-6 (print)ISBN: e-978-1-4244-3124-3 OAI: oai:DiVA.org:liu-44641DiVA: diva2:265503
Conference
47th IEEE Conference on Decision and Control, Cancun, Mexico, December, 2008
Note

©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Henrik Ohlsson, Joakim Rydell, Anders Brun, Jacob Roll, Mats Andersson, Anders Ynnerman and Hans Knutsson, Enabling Bio-Feedback Using Real-Time fMRI, 2008, Proceedings of the 47th IEEE Conference on Decision and Control, 2008, 3336.

Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2015-10-08Bibliographically approved
In thesis
1. Regularization for Sparseness and Smoothness: Applications in System Identification and Signal Processing
Open this publication in new window or tab >>Regularization for Sparseness and Smoothness: Applications in System Identification and Signal Processing
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In system identification, the Akaike Information Criterion (AIC) is a well known method to balance the model fit against model complexity. Regularization here acts as a price on model complexity. In statistics and machine learning, regularization has gained popularity due to modeling methods such as Support Vector Machines (SVM), ridge regression and lasso. But also when using a Bayesian approach to modeling, regularization often implicitly shows up and can be associated with the prior knowledge. Regularization has also had a great impact on many applications, and very much so in clinical imaging. In e.g., breast cancer imaging, the number of sensors is physically restricted which leads to long scantimes. Regularization and sparsity can be used to reduce that. In Magnetic Resonance Imaging (MRI), the number of scans is physically limited and to obtain high resolution images, regularization plays an important role.

Regularization shows-up in a variety of different situations and is a well known technique to handle ill-posed problems and to control for overfit. We focus on the use of regularization to obtain sparseness and smoothness and discuss novel developments relevant to system identification and signal processing.

In regularization for sparsity a quantity is forced to contain elements equal to zero, or to be sparse. The quantity could e.g., be the regression parameter vectorof a linear regression model and regularization would then result in a tool for variable selection. Sparsity has had a huge impact on neighboring disciplines, such as machine learning and signal processing, but rather limited effect on system identification. One of the major contributions of this thesis is therefore the new developments in system identification using sparsity. In particular, a novel method for the estimation of segmented ARX models using regularization for sparsity is presented. A technique for piecewise-affine system identification is also elaborated on as well as several novel applications in signal processing. Another property that regularization can be used to impose is smoothness. To require the relation between regressors and predictions to be a smooth function is a way to control for overfit. We are here particularly interested in regression problems with regressors constrained to limited regions in the regressor-space e.g., a manifold. For this type of systems we develop a new regression technique, Weight Determination by Manifold Regularization (WDMR). WDMR is inspired byapplications in biology and developments in manifold learning and uses regularization for smoothness to obtain smooth estimates. The use of regularization for smoothness in linear system identification is also discussed.

The thesis also presents a real-time functional Magnetic Resonance Imaging (fMRI) bio-feedback setup. The setup has served as proof of concept and been the foundation for several real-time fMRI studies.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. 89 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1351
Keyword
Regularization, sparsity, smothness, lasso, l1, fMRI, bio-feedback
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-60531 (URN)978-91-7393-287-5 (ISBN)
Public defence
2010-11-26, I101, Hus I, Campus Valla, Linköping University, Linköping, 10:15 (Swedish)
Opponent
Supervisors
Available from: 2010-11-03 Created: 2010-10-16 Last updated: 2010-11-03Bibliographically approved

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Ohlsson, HenrikRydell, JoakimBrun, AndersRoll, JacobAndersson, MatsYnnerman, AndersKnutsson, Hans

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