arXiv subjects

Ville Rimpiläinen

Publications and source records attributed to Ville Rimpiläinen.

3 recordsLinked to original sources

arXiv2020

Position Dilution of Precision: a Bayesian point of view

The expected position error in many cases is far from feasible to be estimated experimentally using real satellite measurements which makes the model-based position dilution of precision (PDOP) crucial in positioning and navigation applications. In the following text we derive the relationship between PDOP and position error and we explain that this relationship holds as long as the model for the observation errors represents the true sources of errors.

stat.AP↗

arXiv2017

Bayesian Modelling of Skull Conductivity Uncertainties in EEG Source Imaging

Knowing the correct skull conductivity is crucial for the accuracy of EEG source imaging, but unfortunately, its true value, which is inter- and intra-individually varying, is difficult to determine. In this paper, we propose a statistical method based on the Bayesian approximation error approach to compensate for source imaging errors related to erroneous skull conductivity. We demonstrate the potential of the approach by simulating EEG data of focal source activity and using the dipole scan algorithm and a sparsity promoting prior to reconstruct the underlying sources. The results suggest that the greatest improvements with the proposed method can be achieved when the focal sources are close to the skull.

physics.med-ph↗

arXiv2017

Prior Variances and Depth Un-Biased Estimators in EEG Focal Source Imaging

In electroencephalography (EEG) source imaging, the inverse source estimates are depth biased in such a way that their maxima are often close to the sensors. This depth bias can be quantified by inspecting the statistics (mean and co-variance) of these estimates. In this paper, we find weighting factors within a Bayesian framework for the used L1/L2 sparsity prior that the resulting maximum a posterior (MAP) estimates do not favor any particular source location. Due to the lack of an analytical expression for the MAP estimate when this sparsity prior is used, we solve the weights indirectly. First, we calculate the Gaussian prior variances that lead to depth un-biased maximum a posterior (MAP) estimates. Subsequently, we approximate the corresponding weight factors in the sparsity prior based on the solved Gaussian prior variances. Finally, we reconstruct focal source configurations using the sparsity prior with the proposed weights and two other commonly used choices of weights that can be found in literature.

physics.med-ph↗