From the 1 of 8 linked papers with an AI index.
8 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…
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…
A Complete-Electrode-Model-Based Forward Approach for Transcranial Temporal Interference Stimulation with Linearization: A Numerical Simulation Study
Santtu Söderholm, Maryam Samavaki, Sampsa Pursiainen
Background and Objective: Transcranial temporal interference stimulation (tTIS) is a promising non-invasive brain stimulation technique in which interference between electrical cur…
Multi-Compartment Volume Conductor with Complete Electrode Model: Simulated Stereo-EEG Source Localization using Brainstorm-Zeffiro Plugin
Fernando Galaz Prieto, Takfarinas Medani, Chinmay Chinara +2
This study introduces a novel integration of the Brainstorm (BST) software and the Zeffiro Interface (ZI) to enable whole-head, multi-compartment volume conductor modeling for elec…
In Silico Study for Optimizing Intensity and Focality Electrode Configurations for Directional DBS Under Uncertainty Using Metaheuristic L1L1 Method
Fernando Galaz Prieto, Antti Lassila, Maryam Samavaki +1
Background and Objective: As Deep Brain Stimulation (DBS) advances toward directional leads and optimization-based current steering, selecting electrode contact configurations beco…
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…