Symmetrical EEG-FMRI imaging by sparse regularization. Oberlin, T., Barillot, C., Gribonval, R., & Maurel, P. In 2015 23rd European Signal Processing Conference (EUSIPCO), pages 1870-1874, Aug, 2015.
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Paper doi abstract bibtex
Paper doi abstract bibtex This work considers the problem of brain imaging using simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). To this end, we introduce a linear coupling model that links the electrical EEG signal to the hemodynamic response from the blood-oxygen level dependent (BOLD) signal. Both modalities are then symmetrically integrated, to achieve a high resolution in time and space while allowing some robustness against potential decoupling of the BOLD effect. The novelty of the approach consists in expressing the joint imaging problem as a linear inverse problem, which is addressed using sparse regularization. We consider several sparsity-enforcing penalties, which naturally reflect the fact that only few areas of the brain are activated at a certain time, and allow for a fast optimization through proximal algorithms. The significance of the method and the effectiveness of the algorithms are demonstrated through numerical investigations on a spherical head model.
@InProceedings{7362708,
author = {T. Oberlin and C. Barillot and R. Gribonval and P. Maurel},
booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
title = {Symmetrical EEG-FMRI imaging by sparse regularization},
year = {2015},
pages = {1870-1874},
abstract = {This work considers the problem of brain imaging using simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). To this end, we introduce a linear coupling model that links the electrical EEG signal to the hemodynamic response from the blood-oxygen level dependent (BOLD) signal. Both modalities are then symmetrically integrated, to achieve a high resolution in time and space while allowing some robustness against potential decoupling of the BOLD effect. The novelty of the approach consists in expressing the joint imaging problem as a linear inverse problem, which is addressed using sparse regularization. We consider several sparsity-enforcing penalties, which naturally reflect the fact that only few areas of the brain are activated at a certain time, and allow for a fast optimization through proximal algorithms. The significance of the method and the effectiveness of the algorithms are demonstrated through numerical investigations on a spherical head model.},
keywords = {biomedical MRI;blood;electroencephalography;haemodynamics;inverse problems;medical image processing;spherical head model;linear inverse problem;joint imaging problem;BOLD signal;blood-oxygen level dependent;electrical EEG signal;linear coupling model;functional magnetic resonance imaging;electroencephalography;brain imaging;sparse regularization;EEG-FMRI imaging;Electroencephalography;Brain modeling;Inverse problems;Couplings;Noise measurement;Signal processing algorithms;Imaging;EEG-fMRI;multimodal imaging;structured sparsity;EEG inverse problem},
doi = {10.1109/EUSIPCO.2015.7362708},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103399.pdf},
}Downloads: 0
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