1,720,988 research outputs found
Sequential Monte Carlo samplers for semi-linear inverse problems and application to magnetoencephalography
No full textBayesian smoothing of dipoles in magneto-/electroencephalography
No full textWe describe a novel method for dynamic estimation of multi-dipole states from magneto-/electroencephalography (M/EEG) time series. The new approach builds on the recent development of particle filters for M/EEG; these algorithms approximate, with samples and weights, the posterior distribution of the neural sources at time t given the data up to time t. However, for off-line inference purposes it is preferable to work with the smoothing distribution, i.e. the distribution for the neural sources at time t conditioned on the whole time series. In this study, we use a Monte Carlo algorithm to approximate the smoothing distribution for a time-varying set of current dipoles. We show, using numerical simulations, that the estimates provided by the smoothing distribution are more accurate than those provided by the filtering distribution, particularly at the appearance of the source. We validate the proposed algorithm using an experimental data set recorded from an epileptic patient. Improved localization of the source onset can be particularly relevant in source modeling of epileptic patients, where the source onset brings information on the epileptogenic zone
Computational validation of a particle filtering approach to the solution of the meg inverse problem
No full textBayesian multi-dipole modelling of a single topography in MEG by adaptive sequential Monte Carlo samplers
No full textParticle filters: a new method for reconstructing multiple current dipoles from meg data
No full textWe adapted a Bayesian tracking algorithm called particle filtering for estimating multiple current dipoles from magnetoencephalographic measurements. This method can reconstruct temporally correlated as well as moving dipolar sources in a fully automatic way. Here, we introduce the method and demonstrate its performance by modelling the highly correlated bilateral sources underlying the N100 auditory evoked response
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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