Documentation#

Overview#

CaliBrain is a Python framework for uncertainty estimation and calibration in EEG/MEG inverse source imaging. It supports both:

  • Regression tasks (continuous source estimates)

  • Classification tasks (binary activation detection)

Key Features:

  • Setup of source space, BEM model, forward solution, and leadfield matrices.

  • Simulation of source activity and sensor-level measurements with controllable noise and source orientation (fixed or free).

  • Inverse problem solving and reconstruction of source time courses.

  • Estimation and visualization of confidence intervals and calibration analysis (expected vs. observed coverage).

Supported Inverse Methods#

  • Gamma-MAP

  • eLORETA

  • Bayesian Minimum Norm

Calibration Tasks#

  1. Regression Calibration: - Checks if simulated source currents fall within predicted confidence intervals. - Ideal: Coverage follows the diagonal (Expected vs. Observed).

  2. Classification Calibration: - Assesses if activation probabilities match true activation frequencies. - Ideal: Calibration follows the diagonal.

Main Parameters#

  • Estimator: Gamma-MAP, eLORETA, Bayesian Minimum Norm

  • Orientation: Fixed or Free

  • Noise Type: Oracle, Baseline, Cross-Validation, Joint Learning

  • SNR Level (α): Regularization strength control

  • Active Sources (nnz): Non-zero sources

Outcomes#

  • Regression Calibration Curves (confidence intervals)

  • Classification Calibration Curves (activation probabilities)

  • Quantitative Calibration Metrics

Installation#

For installation, see the Installation Guide.

Usage#

For usage details, refer to the Usage Guide.

Contributing#

We welcome contributions! For guidelines, refer to Contributing Guide.

License#

This project is licensed under the MIT License. See LICENSE.

Citation#

If you use CaliBrain, please cite relevant works in EEG/MEG source imaging and uncertainty quantification.