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- aggregation classification "C1".
- aggregation creator B84140.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2011".
- aggregation format "application/pdf".
- aggregation hasFormat 1219352.bibtex.
- aggregation hasFormat 1219352.csv.
- aggregation hasFormat 1219352.dc.
- aggregation hasFormat 1219352.didl.
- aggregation hasFormat 1219352.doc.
- aggregation hasFormat 1219352.json.
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- aggregation hasFormat 1219352.yaml.
- aggregation isPartOf urn:isbn:9788461461677.
- aggregation language "eng".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Science General".
- aggregation title "Polynomial chaos and Bayesian inference in RPDE's: a biomedical application".
- aggregation abstract "The electroencephalograph (EEG) is one of the most influential tools in the diagnosis of epilepsy and seizures. It measures electrical discharges of neurons in the human brain. The latter consists of many regions, all with a different electrical conductivity. Unfortunately one cannot measure this non invasively, e.g. preoperatively. In this paper, we investigate the uncertainty induced on the location of EEG current dipoles. A Bayesian framework is used, so as to include modeling error and noise, but combined with Polynomial Chaos expansions to represent random variables, speeding up computations. We evaluate this technique on a spherical head model with a standard clinical 27 sensor positioning.".
- aggregation authorList BK216744.
- aggregation aggregates 1267794.
- aggregation isDescribedBy 1219352.
- aggregation similarTo LU-1219352.