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A Brain Computer Interface for Communication Using Real-Time fMRI
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9466-9826
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2010 (English)In: Proceedings of the 20th International Conference on Pattern Recognition, Los Alamitos, CA, USA: IEEE Computer Society, 2010, 3665-3669 p.Conference paper, Published paper (Refereed)
Abstract [en]

We present the first step towards a brain computer interface (BCI) for communication using real-time functional magnetic resonance imaging (fMRI). The subject in the MR scanner sees a virtual keyboard and steers a cursor to select different letters that can be combined to create words. The cursor is moved to the left by activating the left hand, to the right by activating the right hand, down by activating the left toes and up by activating the right toes. To select a letter, the subject simply rests for a number of seconds. We can thus communicate with the subject in the scanner by for example showing questions that the subject can answer. Similar BCI for communication have been made with electroencephalography (EEG). The subject then focuses on a letter while different rows and columns of the virtual keyboard are flashing and the system tries to detect if the correct letter is flashing or not. In our setup we instead classify the brain activity. Our system is neither limited to a communication interface, but can be used for any interface where five degrees of freedom is necessary.

Place, publisher, year, edition, pages
Los Alamitos, CA, USA: IEEE Computer Society, 2010. 3665-3669 p.
Series
International Conference on Pattern Recognition, ISSN 1051-4651
Keyword [en]
Biomedical MRI, Medical image processing, Real-time systems
National Category
Biomedical Laboratory Science/Technology Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-54038DOI: 10.1109/ICPR.2010.894ISBN: 978-1-4244-7542-1 (print)OAI: oai:DiVA.org:liu-54038DiVA: diva2:297936
Conference
20th International Conference on Pattern Recognition, Istanbul, Turkey, 23-26 August 2010
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. Anders Eklund, Mats Andersson, Henrik Ohlsson, Anders Ynnerman and Hans Knutsson, A Brain Computer Interface for Communication Using Real-Time fMRI, 2010, Proceedings from the 20th International Conference on Pattern Recognition (ICPR), 3665-3669. http://dx.doi.org/10.1109/ICPR.2010.894

Available from: 2010-02-19 Created: 2010-02-19 Last updated: 2015-09-22Bibliographically approved
In thesis
1. Signal Processing for Robust and Real-Time fMRI With Application to Brain Computer Interfaces
Open this publication in new window or tab >>Signal Processing for Robust and Real-Time fMRI With Application to Brain Computer Interfaces
2010 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

It is hard to find another research field than functional magnetic resonance imaging (fMRI) that combines so many different areas of research. Without the beautiful physics of MRI we would not have any images to look at in the first place. To get images with good quality it is necessary to fully understand the concepts of the frequency domain. The analysis of fMRI data requires understanding of signal processing and statistics and also knowledge about the anatomy and function of the human brain. The resulting brain activity maps are used by physicians and neurologists in order to plan surgery and to increase their understanding of how the brain works.

This thesis presents methods for signal processing of fMRI data in real-time situations. Real-time fMRI puts higher demands on the signal processing, than conventional fMRI, since all the calculations have to be made in realtime and in more complex situations. The result from the real-time fMRI analysis can for example be used to look at the subjects brain activity in real-time, for interactive planning of surgery or understanding of brain functions. Another possibility is to use the result in order to change the stimulus that is given to the subject, such that the brain and the computer can work together to solve a given task. These kind of setups are often called brain computer interfaces (BCI).

Two BCI are presented in this thesis. In the first BCI the subject was able to balance a virtual inverted pendulum by thinking of activating the left or right hand or resting. In the second BCI the subject in the MR scanner was able to communicate with a person outside the MR scanner, through a communication interface.

Since head motion is common during fMRI experiments it is necessary to apply image registration to align the collected volumes. To do image registration in real-time can be a challenging task, therefore how to implement a volume registration algorithm on a graphics card is presented. The power of modern graphic cards can also be used to save time in the daily clinical work, an example of this is also given in the thesis.

Finally a method for calculating and incorporating a structural based certainty in the analysis of the fMRI data is proposed. The results show that the structural certainty helps to remove false activity that can occur due to head motion, especially at the edge of the brain.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. 130 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1432
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-54040 (URN)LIU-TEK-LIC-2010:3 (Local ID)978-91-7393-431-2 (ISBN)LIU-TEK-LIC-2010:3 (Archive number)LIU-TEK-LIC-2010:3 (OAI)
Presentation
2010-03-09, Wranne-salen, CMIV, plan 11, Campus US, Linköpings universitet, Linköping, 14:00 (English)
Opponent
Supervisors
Available from: 2010-02-19 Created: 2010-02-19 Last updated: 2013-08-28Bibliographically approved
2. Computational Medical Image Analysis: With a Focus on Real-Time fMRI and Non-Parametric Statistics
Open this publication in new window or tab >>Computational Medical Image Analysis: With a Focus on Real-Time fMRI and Non-Parametric Statistics
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Functional magnetic resonance imaging (fMRI) is a prime example of multi-disciplinary research. Without the beautiful physics of MRI, there wouldnot be any images to look at in the first place. To obtain images of goodquality, it is necessary to fully understand the concepts of the frequencydomain. The analysis of fMRI data requires understanding of signal pro-cessing, statistics and knowledge about the anatomy and function of thehuman brain. The resulting brain activity maps are used by physicians,neurologists, psychologists and behaviourists, in order to plan surgery andto increase their understanding of how the brain works.

This thesis presents methods for real-time fMRI and non-parametric fMRIanalysis. Real-time fMRI places high demands on the signal processing,as all the calculations have to be made in real-time in complex situations.Real-time fMRI can, for example, be used for interactive brain mapping.Another possibility is to change the stimulus that is given to the subject, inreal-time, such that the brain and the computer can work together to solvea given task, yielding a brain computer interface (BCI). Non-parametricfMRI analysis, for example, concerns the problem of calculating signifi-cance thresholds and p-values for test statistics without a parametric nulldistribution.

Two BCIs are presented in this thesis. In the first BCI, the subject wasable to balance a virtual inverted pendulum by thinking of activating theleft or right hand or resting. In the second BCI, the subject in the MRscanner was able to communicate with a person outside the MR scanner,through a virtual keyboard.

A graphics processing unit (GPU) implementation of a random permuta-tion test for single subject fMRI analysis is also presented. The randompermutation test is used to calculate significance thresholds and p-values forfMRI analysis by canonical correlation analysis (CCA), and to investigatethe correctness of standard parametric approaches. The random permuta-tion test was verified by using 10 000 noise datasets and 1484 resting statefMRI datasets. The random permutation test is also used for a non-localCCA approach to fMRI analysis.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 119 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1439
Keyword
functional magnetic resonance imaging, brain computer interfaces, canonical correlation analysis, random permutation test, graphics processing unit
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-76120 (URN)978-91-7519-921-4 (ISBN)
Public defence
2012-04-27, Eken, Campus US, Linköping University, Linköping, 09:00 (English)
Opponent
Supervisors
Available from: 2012-03-28 Created: 2012-03-28 Last updated: 2013-08-28Bibliographically approved

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Eklund, AndersAndersson, MatsOhlsson, HenrikYnnerman, AndersKnutsson, Hans

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