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

Autor
Escuain-Poole, L.; Garcia, J.; Pons, A. J.
Tipus d'activitat
Article en revista
Revista
Frontiers in Applied Mathematics and Statistics
Data de publicació
2018-10-15
Volum
4
Pàgina inicial
1
Pàgina final
15
DOI
https://doi.org/10.3389/fams.2018.00046 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/122761 Obrir en finestra nova
https://arxiv.org/abs/1708.05282 Obrir en finestra nova
URL
https://www.frontiersin.org/articles/10.3389/fams.2018.00046/full Obrir en finestra nova
Resum
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...
Citació
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.
Paraules clau
Data assimilation, EEG, Neural mass model, Parameter estimation, Unscented Kalman filter
Grup de recerca
CEBIM - Centre de Biotecnologia Molecular
DONLL - Dinàmica no Lineal, Òptica no Lineal i Làsers

Participants

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