Weighted time series analysis for electroencephalographic source localization. Giraldo, E., Peluffo-Ordoñez, D., & Castellanos-Domínguez, G. (Prueba) DYNA (Prueba), 2012.
Weighted time series analysis for electroencephalographic source localization [link]Website  abstract   bibtex   
This paper presents a new method to estimate neural activity from electroencephalographic signals using a weighted time series analysis. The method considers a physiologically based linear model that takes both spatial and temporal dynamics into account and a weighting stage to modify the assumptions of the model from observations. The calculated weighting matrix is included in the cost function used to solve the dynamic inverse problem, and therefore in the Kalman filter formulation. In this way, a weighted Kalman filtering approach is proposed including a preponderance matrix. The filter's performance (in terms of localization error) is analyzed for several SNRs. The optimal performance is achieved using the linear model with a weighting matrix computed by an inner product method.
@article{
 title = {Weighted time series analysis for electroencephalographic source localization},
 type = {article},
 year = {2012},
 keywords = {Brain mapping,Inverse problem,Weighting matrix},
 websites = {http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0012-73532012000600008},
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 abstract = {This paper presents a new method to estimate neural activity from electroencephalographic signals using a weighted time series analysis. The method considers a physiologically based linear model that takes both spatial and temporal dynamics into account and a weighting stage to modify the assumptions of the model from observations. The calculated weighting matrix is included in the cost function used to solve the dynamic inverse problem, and therefore in the Kalman filter formulation. In this way, a weighted Kalman filtering approach is proposed including a preponderance matrix. The filter's performance (in terms of localization error) is analyzed for several SNRs. The optimal performance is achieved using the linear model with a weighting matrix computed by an inner product method.},
 bibtype = {article},
 author = {Giraldo, Eduardo and Peluffo-Ordoñez, Diego and Castellanos-Domínguez, Germán},
 journal = {(Prueba) DYNA (Prueba)}
}

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