14 citations · 41 across the 16 of their papers we have counts for
16 papers
Overview of Bayesian Solvers in EEG Distributed Source Models: Prior Selection, Algorithmic Implementation, and Depth Bias Reduction
Joonas Lahtinen, Alexandra Koulouri
Electroencephalography (EEG) source imaging aims to reconstruct the spatial distribution of neural activity within the brain from non-invasive scalp measurements. This inverse prob…
Tracking EEG Thalamic and Cortical Focal Brain Activity using Standardized Kalman Filtering with Kinematics Modeling
Veikka Piispa, Dilshanie Prasikala, Joonas Lahtinen +2
Kalman filtering has proven to be effective for estimating brain activity using EEG recordings. In particular, the introduced post hoc standardization step of the algorithm, inspir…
The Effect of Prior Parameters on Standardized Kalman Filter-Based EEG Source Localization
Dilshanie Prasikala, Joonas Lahtinen, Alexandra Koulouri +1
EEG Source localization is a critical tool in neuroscience, with applications ranging from epilepsy diagnosis to cognitive research. It involves solving an ill-posed inverse proble…
Bayesian Model Parameter Learning in Linear Inverse Problems: Application in EEG Focal Source Imaging
Alexandra Koulouri, Ville Rimpilainen
Inverse problems can be described as limited-data problems in which the signal of interest cannot be observed directly. A physics-based forward model that relates the signal with t…
Vector tomography for reconstructing electric fields with non-zero divergence in bounded domains
Alexandra Koulouri, Mike Brookes, Ville Rimpilainen
In vector tomography (VT), the aim is to reconstruct an unknown multi-dimensional vector field using line integral data. In the case of a 2-dimensional VT, two types of line integr…
Conditionally Exponential Prior in Focal Near- and Far-Field EEG Source Localization via Randomized Multiresolution Scanning (RAMUS)
Joonas Lahtinen, Alexandra Koulouri, Atena Rezaei +1
In this paper, we focus on the inverse problem of reconstructing distributional brain activity with cortical and weakly detectable deep components in non-invasive Electroencephalog…
Real-time Ionospheric Imaging of S4 Scintillation from Limited Data with Parallel Kalman Filters and Smoothness
Alexandra Koulouri
In this paper, we propose a Bayesian framework to create two dimensional ionospheric images of high spatio-temporal resolution to monitor ionospheric irregularities as measured by…
Adaptive Superresolution in Deconvolution of Sparse Peaks
Alexandra Koulouri, Pia Heins, Martin Burger
The aim of this paper is to investigate superresolution in deconvolution driven by sparsity priors. The observed signal is a convolution of an original signal with a continuous ker…
Randomized Multiresolution Scanning in Focal and Fast E/MEG Sensing of Brain Activity with a Variable Depth
Atena Rezaei, Alexandra Koulouri, Sampsa Pursiainen
We focus on electromagnetoencephalography imaging of the neural activity and, in particular, finding a robust estimate for the primary current distribution via the hierarchical Bay…
Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the help of Bayesian Uncertainty Modelling
Alexandra Koulouri, Ville Rimpilainen
The electroencephalography (EEG) source imaging problem is very sensitive to the electrical modelling of the skull of the patient under examination. Unfortunately, the currently av…
Electrical Impedance Tomography with Box Constraint for Skull Conductivity Estimation
Ville Rimpilainen, Theodoros Samaras, Alexandra Koulouri
Unknown electric conductivities of human tissues is a common issue in medical engineering. Electrical impedance tomography (EIT) is an imaging modality that can be used to determin…
Position Dilution of Precision: a Bayesian point of view
Alexandra Koulouri, Ville Rimpiläinen, Nathan D. Smith
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 pre…
Methodology to estimate ionospheric scintillation risk maps and their contribution to position dilution of precision on the ground
Alexandra Koulouri, Nathan D. Smith, Bruno C. Vani +3
Satellite-based communications, navigation systems and many scientific instruments rely on observations of trans-ionospheric signals. The quality of these signals can be deteriorat…
Overcoming the ill-posedness through discretization in vector tomography: Reconstruction of irrotational vector fields
Alexandra Koulouri
Vector tomography methods intend to reconstruct and visualize vector fields in restricted domains by measuring line integrals of projections of these vector fields. Here, we deal w…
Prior Variances and Depth Un-Biased Estimators in EEG Focal Source Imaging
Alexandra Koulouri, Ville Rimpiläinen, Mike Brookes +1
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…
Bayesian Modelling of Skull Conductivity Uncertainties in EEG Source Imaging
Ville Rimpiläinen, Alexandra Koulouri, Felix Lucka +2
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 diff…