Bayesian EEG Source Model Comparisons with Simulated Data and MATLAB Scripts

MATLAB scripts and simulated data from a review paper comparing Bayesian solvers for EEG distributed source models. The dataset includes inversion methods like Weighted Minimum Norm Estimate and Conditional Laplace, along with visualization scripts and simulated observations. Author Joonas Lahtinen published this resource on Harvard Dataverse in April 2026.

Use Cases

• Benchmarking Bayesian inversion algorithms based on the included Weighted Minimum Norm Estimate and Conditional Laplace methods.
• Visualizing EEG source model results using the provided MATLAB visualization scripts.
• Testing depth bias reduction techniques based on the simulated example observations and lead fields.
• Comparing algorithmic implementations of Conditional Gaussian and Group Laplace priors as described.

Strengths

• Includes five specific inversion methods: Weighted Minimum Norm Estimate, Conditional Gaussian, Conditional Laplace, Conditional Group Laplace, and Group Laplace.
• Provides both forward and inverse lead fields, along with simulated example observations for demonstrative comparisons.
• Linked to a GitHub repository (https://github.com/JoonasJL/EEG-MEG-Inversion-Tools) for additional inversion methods.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file formats are unknown, which may limit suitability assessment.
• Data may reflect methodological bias inherent to the specific numerical settings and simulations used.

Provenance

Source

Harvard Dataverse, author Joonas Lahtinen.

Collection Method

Created for the "Comparison of Bayesian Solvers in EEG Distributed Source Models" review paper, likely involving numerical simulation.

Time Range

null

Freshness

Last updated 2026-04-13 15:14:11; freshness should be verified.

Geography

null