A review paper on deep learning in neuroimaging data analysis
The paper entitled Deep learning in neuroimaging data analysis: Applications, challenges, and solutions by Lev Kiar Avberšek and Grega Repovš has been published in Frontiers in Neuroimaging. The paper provides an overview of the use of deep learning as a form of machine learning in the analysis of neuroimaging data.
R package for the construction and evaluation of linear models for the analysis of task-related fMRI data
We published a paper in Frontiers in Neuroimaging with a title autohrf-an R package for generating data-informed event models for general linear modeling of task-based fMRI data.
The paper presents an original package for the R programming language that allows the evaluation of linear models and the automatic calculation of
Nov
28
2022
New paper on the MR-induced EEG artifact reduction
A new paper from our lab was recently published in Frontiers in Neuroimaging titled "Evaluation and comparison of most prevalent artifact reduction methods for EEG acquired simultaneously with fMRI". An in-depth evaluation of the most common approaches to MR-induced EEG artifact reduction utilizing Bayesian hierarchical probabilistic modeling.
The paper is
Nov
14
2022
A review paper on deep learning in neuroimaging data analysis
The paper entitled Deep learning in neuroimaging data analysis: Applications, challenges, and solutions by Lev Kiar Avberšek and Grega Repovš has been published in Frontiers in Neuroimaging. The paper provides an overview of the use of deep learning as a form of machine learning in the analysis of neuroimaging data.
