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Extracranial estimation of neural mass model parameters using the unscented kalman filter

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dc.contributor.author Escuain-Poole, Lara
dc.contributor.author García Ojalvo, Jordi
dc.contributor.author Pons, Antonio J.
dc.date.accessioned 2019-06-19T08:14:52Z
dc.date.available 2019-06-19T08:14:52Z
dc.date.issued 2018
dc.identifier.citation Escuain-Poole L, Garcia-Ojalvo J, Pons AJ. Extracranial estimation of neural mass model parameters using the unscented kalman filter. Frontiers in Applied Mathematics and Statistics. 2018; 4:46. DOI 10.3389/fams.2018.00046
dc.identifier.issn 2297-4687
dc.identifier.uri http://hdl.handle.net/10230/41837
dc.description.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 electroencephalography (EEG) measurements to infer brain states and their characteristics. For this purpose, we use Kalman filtering to combine synthetic EEG data with a coupled neural-mass model together with Ary's model of the head, which projects intracranial signals onto the scalp. Our results show that using several extracranial electrodes allows to successfully estimate the state and parameters of the neural masses and their interactions, whereas one single electrode provides only a very partial and insufficient view of the system. The superiority of using multiple extracranial electrodes over using only one, be it intra- or extracranial, is shown over a wide variety of dynamical behaviours. Our results show potential towards future clinical applications of the method.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Frontiers
dc.relation.ispartof Frontiers in Applied Mathematics and Statistics. 2018; 4:46
dc.rights © 2018 Escuain-Poole, Garcia-Ojalvo and Pons. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
dc.title Extracranial estimation of neural mass model parameters using the unscented kalman filter
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.3389/fams.2018.00046
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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