3 citations · 3 across the 2 of their papers we have counts for
4 papers
Exact Spectral Uncertainty Decomposition for Linear-Gaussian Bayesian M/EEG Inverse Problems
Ismail Huseynov, Stefan Haufe
Distributed magneto- and electroencephalographic (M/EEG) source reconstruction is a severely underdetermined linear inverse problem. Bayesian source imaging regularizes inversion t…
Structure-Preserving Correction Learning for Sparse Bayesian Inference in Brain Source Imaging
Marco Morik, Xiao Ruiting, Shinichi Nakajima +2
Classical sparse Type-II Bayesian methods for M/EEG brain imaging support joint estimation of source and noise hyperparameters, but rely on fixed iterative update rules. Although t…
Minimizing False-Positive Attributions in Explanations of Non-Linear Models
Anders Gjølbye, Stefan Haufe, Lars Kai Hansen
Suppressor variables can influence model predictions without being dependent on the target outcome, and they pose a significant challenge for Explainable AI (XAI) methods. These va…
Enhancing Brain Source Reconstruction by Initializing 3D Neural Networks with Physical Inverse Solutions
Marco Morik, Ali Hashemi, Klaus-Robert Müller +2
Reconstructing brain sources is a fundamental challenge in neuroscience, crucial for understanding brain function and dysfunction. Electroencephalography (EEG) signals have a high…