From the 1 of 7 linked papers with an AI index.
7 papers
Advanced EEG Source Models from the Perspective of FEM and Inverse Solutions
Santtu Söderholm, Joonas Lahtinen, Sampsa Pursiainen
The paper compares finite element forward models for EEG using different source representations and evaluates several inverse localization methods, finding that matching the source…
On Unbiased Parameter Estimation and Signal Reconstruction
Joonas Lahtinen
In this paper, we expand the theory of depth-unbiased source localization to unbiased parameter estimation and signal reconstruction of an arbitrary number of non-zero parameters t…
Forward--Inverse Interplay in FEM-Based EEG Source Imaging: Distributional Signatures of Advanced Source Models and Inverse Solvers
Santtu Söderholm, Joonas Lahtinen, Sampsa Pursiainen
Electroencephalography (EEG) source imaging aims to infer brain activity from electrical potentials measured on the scalp. This is a difficult problem because many different source…
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…
Stable EEG Source Estimation for Standardized Kalman Filter using Change Rate Tracking
Joonas Lahtinen
This article focuses on the measurement and evolution modeling of Standardized Kalman filtering for brain activity estimation using non-invasive electroencephalography data. Here,…
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…