Alexandra Koulouri

• Dipl. Eng. MSc PhD
• Academy Post-Doc Researcher at Tampere University

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 the observations is typically needed. Unfortunately, unknown model parameters and imperfect forward models can undermine the signal recovery. Even though supervised machin...

Objective Unknown conductivities of the head tissues, particularly the skull, is a major factor of uncertainty in electroencephalography (EEG) source imaging. Here, we develop a personalized skull conductivity framework aiming to improve the head models used in the EEG source imaging and to reduce localization errors. Methods We employ Electrical...

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 integral data are usually required. These data correspond to integration of the parallel and perpendicular projection of the vector field along integration lines. VT methods ar...

Objective: To investigate the ability of standardization to reduce source localization errors and measurement noise uncertainties for hierarchical Bayesian algorithms with L2- and L1-norms as priors in electroencephalography and magnetoencephalography of focal epilepsy. Methods: Description of the standardization methodology relying on the Hierarc...

p>In this article, we present a new Bayesian filtering method for non-invasive electroencephalography (EEG) that is capable of reconstructing spatiotemporal brain activity, including both cortical and weakly detectable deep components. Our approach improves upon the standard Bayesian recursive filtering method, also known as Kalman filtering, by in...

p>In this article, we present a new Bayesian filtering method for non-invasive electroencephalography (EEG) that is capable of reconstructing spatiotemporal brain activity, including both cortical and weakly detectable deep components. Our approach improves upon the standard Bayesian recursive filtering method, also known as Kalman filtering, by in...

In this paper, we focus on the inverse problem of reconstructing distributional brain activity with cortical and weakly detectable deep components in non-invasive Electroencephalography. We consider a recently introduced hybrid reconstruction strategy combining a hierarchical Bayesian model to incorporate a priori information and the advanced rando...

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 the S4 index. Here, we recast the standard Bayesian recursive filtering for a linear Gaussian state-space model, also referred to as the Kalman filter, first by augmentin...

This study concerns reconstructing brain activity at various depths based on non-invasive EEG (electroencephalography) scalp measurements. We aimed at demonstrating the potential of the RAMUS (randomized multiresolution scanning) technique in localizing weakly distinguishable far-field sources in combination with coninciding cortical activity. As w...

This paper develops mathematical methods for localizing focal sources at different depths based on the non-invasive electro-/magnetoencephalography measurements. In the context of hierarchical Bayesian modelling, we introduce a conditionally exponential prior (CEP) which extends the concept of the conditionally Gaussian prior (CGP) and has been pro...

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 the S4 index. Here, we recast the standard Bayesian recursive filtering for a linear Gaussian state-space model, also referred to as the Kalman filter, first by augmentin...

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 determine these conductivities in vivo from boundary measurements. In this paper, we demonstrate that local conductivity values of different skull segments can be solved from EIT...

The electroencephalography (EEG) source imaging problem is very sensitive to the electrical modelling of the skull of the patient under examination. Unfortunately, the currently available EEG devices and their embedded software do not take this into account; instead, it is common to use a literature-based skull conductivity parameter. In this paper...

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 kernel. With the prior knowledge that the original signal can be considered as a sparse combination of Dirac delta peaks, we seek to estimate the positions and amplitudes of...

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 kernel.With the prior knowledge that the original signal can be considered as a sparse combination of Dirac delta peaks, we seek to estimate the positions and amplitudes of...

We focus on electro-/magnetoencephalography imaging of the neural activity and, in particular, finding a robust estimate for the primary current distribution via the hierarchical Bayesian model (HBM). Our aim is to develop a reasonably fast maximum a posteriori (MAP) estimation technique which would be applicable for both superficial and deep areas...

Correction to: Journal of Geodesy (2020) 94:22

We focus on electromagnetoencephalography imaging of the neural activity and, in particular, finding a robust estimate for the primary current distribution via the hierarchical Bayesian model (HBM). Our aim is to develop a reasonably fast maximum a posteriori (MAP) estimation technique which would be applicable for both superficial and deep areas w...

Satellite-based communications, navigation systems and many scientific instruments rely on observations of trans-ionospheric signals. The quality of these signals can be deteriorated by ionospheric scintillation which can have detrimental effects on the mentioned applications. Therefore, monitoring of ionospheric scintillation and quantifying its e...

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 determine these conductivities in vivo from boundary measurements. In this paper, we demonstrate that local conductivity values of different skull segments can be solved from EIT...

The electroencephalography (EEG) source imaging problem is very sensitive to the electrical modelling of the skull of the patient under examination. Unfortunately, the currently available EEG devices and their embedded software do not take this into account; instead, it is common to use a literature-based skull conductivity parameter. In this paper...

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 thi...

Satellite-based communications, navigation systems and many scientific instruments rely on observations of trans-ionospheric signals. The quality of these signals can be deteriorated by ionospheric scintillation which can have detrimental effects on the mentioned applications. Therefore, monitoring of ionospheric scintillation and quantifying its e...

Electroencephalography (EEG) source imaging is an ill-posed inverse problem that requires accurate conductivity modelling of the head tissues, especially the skull. Unfortunately, the conductivity values are difficult to determine in vivo. In this paper, we show that the exact knowledge of the skull conductivity is not always necessary when the Bay...

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 e...

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 covariance) of these estimates. In this paper, we find weighting factors within a Bayesian framework for the used \(\el...

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 with the reconstruction of irrotational vector functions from boundary measurements. As the majority of inverse problems, vector field recovery is an ill posed in the cont...

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 e...

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/L...

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 integral data are usually required. These data correspond to integration of the parallel and perpendicular projection of the vector field along the integration lines and are ca...

In the inverse source problem of the Poisson equation, measurements on the domain boundaries are used to reconstruct sources inside the domain. The problem is an ill-posed inverse problem and it is sensitive to modelling errors of the domain. These errors can be boundary, structure and material property errors, for example. In this paper, we invest...

https://spiral.imperial.ac.uk/handle/10044/1/25759

In this paper, we show that the estimation of an irrotational vector field employing the longitudinal ray transform in the discrete domain is tractable, despite the fact that this problem cannot be solved in the continuous domain using the same formulation. We derive a set of algebraic equations and solve the ill conditioned inverse problem by dire...

We revisit the problem of the reconstruction of an irrotational vector field by solving a set of line integral equations in the discrete domain. We show that the continuous inverse Radon formulation fails to reconstruct an irrotational vector field while the approximate solution of the problem in the digital domain is feasible, overcoming the intri...

In the present project, the final goal is the implementation of a different approach for the EEG (Electroencephalography) analysis employing the proposed vector field method. Rather than estimating strengths or locations of the electric sources inside the brain, which is a very complicated task, a reconstruction of the corresponding static bioelect...

We present a novel method for the automatic segmentation of the thoracic cavity and the detection of human lungs and the major thoracic organs, as a necessary pre-processing step for a subsequent deformable registration scheme. Our method is divided into two parts. In the first stage, a coarse separation of the body into two subregions, one with th...

We present a method for the segmentation and the detection of human Abdominal Aortia in CT images. Our method is divided into two parts. In the first part we estimate the position and the dimension of the aortic lumen using state-of-the-art object tracking techniques. The second part employs curve fitting methods in order to detect the boundaries o...