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Extracranial Estimation of Neural Mass Model Parameters Using the Unscented Kalman Filter

Author
Escuain-Poole, L.; Garcia, J.; Pons, A. J.
Type of activity
Journal article
Journal
Frontiers in Applied Mathematics and Statistics
Date of publication
2018-10-15
Volume
4
First page
1
Last page
15
DOI
https://doi.org/10.3389/fams.2018.00046 Open in new window
Repository
http://hdl.handle.net/2117/122761 Open in new window
https://arxiv.org/abs/1708.05282 Open in new window
URL
https://www.frontiersin.org/articles/10.3389/fams.2018.00046/full Open in new window
Abstract
Data assimilation, defined as the fusion of data with preexisting knowledge, is particularly suited to elucidating underlying phenomena from noisy/insufficient observations. Although this approach has been widely used in diverse fields, only recently have efforts been directed to problems in neuroscience, using mainly intracranial data and thus limiting its applicability to invasive measurements involving electrode implants. Here we intend to apply data assimilation to non-invasive electroenceph...
Citation
Escuain-Poole, L., Garcia, J., Pons, A. J. Extracranial Estimation of Neural Mass Model Parameters Using the Unscented Kalman Filter. "Frontiers in Applied Mathematics and Statistics", 15 Octubre 2018, vol. 4, p. 1-15.
Keywords
Data assimilation, EEG, Neural mass model, Parameter estimation, Unscented Kalman filter
Group of research
CEBIM - Molecular Biotechnology Centre
DONLL - Nonlinear dynamics, nonlinear optics and lasers

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