Read Me

Overview

gaitmod 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 gaitmod, please cite relevant works in EEG/MEG source imaging and uncertainty quantification.