5 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…
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…
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…