TBME
IEEE Transactions on Biomedical Engineering contains basic and applied papers dealing with biomedical engineering. Papers range from engineering development in methods and techniques with biomedical applications to experimental and clinical investigations with engineering contributions.
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Elvira Pirondini
E. Pirondini is with the Institute of Bioengineering and the Center for Neuroprosthetics, Ecole Polytechnique Federale de Lausanne, and also with the Department of Radiology and Medical Informatics, University of Geneva.
Associated articles
TBME, Featured Articles
Computationally Efficient Algorithms for Sparse, Dynamic Solutions to the EEG Source Localization Problem
Elvira Pirondini, Behtash Babadi, Gabriel Obregon-Henao, Camilo Lamus, Wasim Q. Malik, Matti S. Hämäläinen, Patrick L. Purdon
Electroencephalography (EEG) and magnetoencephalography noninvasively record scalp electromagnetic fields generated by cerebral currents, revealing millisecond-level brain dynamics useful for neuroscience and clinical applications. Estimating the currents that generate these fields, i.e., source localization, is an ill-conditioned inverse problem. Solutions to...
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Posted on 22 MAY 2018
TBME,
Computationally Efficient Algorithms for Sparse, Dynamic Solutions to the EEG Source Localization Problem
Elvira Pirondini, Behtash Babadi, Gabriel Obregon-Henao, Camilo Lamus, Wasim Q. Malik, Matti S. Hämäläinen, Patrick L. Purdon
Estimating the currents that underlie the field potentials captured by electroencephalography (EEG), i.e., EEG source localization, is an ill-conditioned inverse problem. Existing solutions consider spatial continuity constraints, dynamic modeling, or sparsity constraints. The computational cost of combining these approaches, however, poses a challenge for practical applications. We propose a new computationally efficient EEG source localization method that employs spatial covariance estimation, state-space modeling, and sparsity-enforcing priors. We validate the performance of our method using both simulated and experimentally recorded EEG data. Our approach provides substantial performance improvements over existing methods and thereby facilitates practical applications in both neuroscience and medicine...
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Posted on 2 MAR 2020