Weighted Filtering for Neural Activity Reconstruction Under Time Varying Constraints View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

J. I. Padilla-Buritica , E. Giraldo-Suárez , G. Castellanos-Dominguez

ABSTRACT

A novel Weighted Unscented Kalman Filtering method is introduced for neural activity estimation from electroencephalographic signals. The introduction of a weighting stage improves the solution by extracting relevant information directly from the measured data. Besides, a discrete nonlinear state space model representing the brain neural activity is used as a physiological constraint in order to improve the estimation. Moreover, time-varying parameters are considered which allow describing adequately healthy and pathological activity even for localized epilepsy events. Performance of the new method is evaluated in terms of introduced error measurements by application to simulated EEG data over several noise conditions. As a result, a considerable improvement over linear estimation approaches is found. More... »

PAGES

377-387

References to SciGraph publications

Book

TITLE

Artificial Computation in Biology and Medicine

ISBN

978-3-319-18913-0
978-3-319-18914-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-18914-7_40

DOI

http://dx.doi.org/10.1007/978-3-319-18914-7_40

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1033345668


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