Neural Activity Estimation from EEG Using an Iterative Dynamic Inverse Problem Solution View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

E. Giraldo-Suárez , G. Castellanos-Dominguez

ABSTRACT

Estimation of neural activity using Electroencephalography (EEG) signals allows identifying with high temporal resolution those brain structures related to pathological states. This work aims to improve spatial resolution of estimated neural activity employing time-varying dynamic constraints within the iterative inverse problem framework. Particularly, we introduce the use of Dynamic Neural Fields (DNF) to represent neural activity directly related to epileptic foci localization adequately. So, we develop a DNF-based time variant estimation model in the form of an Iterative Regularization Algorithm (IRA) that carries out neural activity estimation at every time EEG sample. The IRA model performance that is evaluated on simulated and real cases is compared with the baseline static and dynamic methods under several noise conditions. To this end, we use different error measures showing that the IRA estimation model can be more accurate and robust than the other compared methods. More... »

PAGES

388-397

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_41

DOI

http://dx.doi.org/10.1007/978-3-319-18914-7_41

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1019625275


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