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dc.contributor.authorSoler Guevara, Andres Felipe
dc.contributor.authorGiraldo, Eduardo
dc.contributor.authorMolinas Cabrera, Maria Marta
dc.date.accessioned2020-01-29T09:46:51Z
dc.date.available2020-01-29T09:46:51Z
dc.date.created2019-07-04T16:30:22Z
dc.date.issued2019
dc.identifier.citationLecture Notes in Computer Science (LNCS). 2019, 11466 LNBI 397-406.nb_NO
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11250/2638508
dc.description.abstractThe underlying activity in the brain can be estimated using methods based on discrete physiological models of the neural activity. These models involve parameters for weighting the estimated source activity of previous samples, however, those parameters are subject- and task-dependent. This paper introduces a dynamical non-linear regularized observer (DYNLO), through the implementation of an Extended Kalman Filter (EKF) for estimating the model parameters of the dynamical source activity over the neural activity reconstruction performed by a non-linear regularized observer (NLO). The proposed methodology has been evaluated on real EEG signals using a realistic head model. The results have been compared with least squares (LS) for model parameter estimation with NLO and the multiple sparse prior (MSP) algorithm for source estimation. The correlation coefficient and relative error between the original EEG and the estimated EEG from the source reconstruction were inspected and the results show an improvement of the solution in terms of the aforementioned measurements and a reduction of the computational time.nb_NO
dc.language.isoengnb_NO
dc.publisherSpringer Verlagnb_NO
dc.titleDYNLO: Enhancing Non-linear Regularized State Observer Brain Mapping Technique by Parameter Estimation with Extended Kalman Filternb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber397-406nb_NO
dc.source.volume11466 LNBInb_NO
dc.source.journalLecture Notes in Computer Science (LNCS)nb_NO
dc.identifier.doi10.1007/978-3-030-17935-9_36
dc.identifier.cristin1710220
dc.description.localcodeThis is a post-peer-review, pre-copyedit version of an article. Locked until 13.4.2020 due to copyright restrictions. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-17935-9_36nb_NO
cristin.unitcode194,63,25,0
cristin.unitnameInstitutt for teknisk kybernetikk
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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