This tutorial walks through the process of setting up a forward model and simulating a lead field matrix using the MNE-Python library, a foundational step for source estimation tasks in CaliBrain.

Learn how to generate realistic, multi-trial brain source signals with event-related potential (ERP) characteristics. This is crucial for testing the performance of source reconstruction algorithms in a controlled environment.

This guide covers the application of various inverse methods available in CaliBrain to reconstruct brain source activity from simulated M/EEG data. Compare different algorithms and understand their outputs.

A key feature of CaliBrain is uncertainty quantification. This tutorial demonstrates how to visualize the uncertainty of source estimates by plotting credible intervals and assess model calibration with calibration curves.

This example demonstrates the distinct forms of information captured by coherency-based connectivity methods, and highlights the scenarios in which these different methods should be applied.

This example demonstrates how canonical coherency (CaCoh) - a multivariate method based on coherency - can be used to compute connectivity between whole sets of sensors, alongside spatial patterns of the connectivity.