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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).
- Solving the inverse problem and reconstructing source time courses.
- Estimation and visualization of confidence intervals.
- Calibration analysis by comparing expected vs. observed confidence levels.
- Gamma-MAP
- eLORETA
- Bayesian Minimum Norm
- Check if true simulated source currents fall within predicted confidence intervals.
- Plot calibration curve (Expected vs. Observed coverage).
- Well-calibrated models should follow the diagonal.
- Assess if estimated activation probabilities match true activation frequencies.
- Plot calibration curve for activation detection.
- Ideal calibration follows the diagonal.
- Estimator: Gamma-MAP, eLORETA, Bayesian Minimum Norm
- Orientation: Fixed or Free
- Noise Type: Oracle, Baseline, Cross-Validation, Joint Learning
- SNR Level (α): Control regularization strength
- Active Sources (nnz): Number of nonzero sources
- Regression Calibration Curves (confidence intervals)
- Classification Calibration Curves (activation probabilities)
- Quantitative Calibration Metrics
Please see the Installation Guide.
Please see the Usage Guide.
We welcome contributions! Please see CONTRIBUTING.md.
This project is licensed under the GNU Affero General Public License v3.0. See LICENSE.
If you use CaliBrain, please cite relevant works in EEG/MEG source imaging and uncertainty quantification.