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Open AccessArticle

Less Is Enough: Assessment of the Random Sampling Method for the Analysis of Magnetoencephalography (MEG) Data

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Dipartimento di Matematica “Tullio Levi-Civita”, Università di Padova, 35131 Padova, Italy
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Padova Neuroscience Center (PNC), Università di Padova, 35131 Padova, Italy
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Istituto per le Applicazioni del Calcolo (IAC) “M. Picone”, 00185 Roma, Italy
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Dipartimento SBAI, Università di Roma “La Sapienza”, 00161 Roma, Italy
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Author to whom correspondence should be addressed.
Math. Comput. Appl. 2019, 24(4), 98; https://doi.org/10.3390/mca24040098
Received: 19 April 2019 / Revised: 13 November 2019 / Accepted: 16 November 2019 / Published: 19 November 2019
Magnetoencephalography (MEG) aims at reconstructing the unknown neuroelectric activity in the brain from non-invasive measurements of the magnetic field induced by neural sources. The solution of this ill-posed, ill-conditioned inverse problem is usually dealt with using regularization techniques that are often time-consuming, and computationally and memory storage demanding. In this paper we analyze how a slimmer procedure, random sampling, affects the estimation of the brain activity generated by both synthetic and real sources. View Full-Text
Keywords: inverse problem; random sampling; neuroimaging; magnetoencephalography inverse problem; random sampling; neuroimaging; magnetoencephalography
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Campi, C.; Pascarella, A.; Pitolli, F. Less Is Enough: Assessment of the Random Sampling Method for the Analysis of Magnetoencephalography (MEG) Data. Math. Comput. Appl. 2019, 24, 98.

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