v0.1.1

ERP-like EEG Data Simulation & Enhancements

This release introduces enhancement to the data simulation for a more realistic ERP-like EEG signals. It also includes improvements to noise handling and refactoring of the data simulation pipeline by @orabe (see PR #7).

Key Enhancements:

• ERP-like EEG Data Simulation (Closes #6):
  • Source-Level ERP Generation: Implemented a pipeline to generate plausible ERP signals at selected sources. This involves:
    • Generating band-limited, temporally windowed white noise.
    • Applying Butterworth bandpass filtering.
    • Windowing with a Hanning window (now supporting random length and duration) for smooth onsets/offsets.
    • Normalization and amplitude scaling.
  • Sensor-Level Projection: Projecting simulated source activity to the sensor level using the leadfield matrix.
  • Noise Modeling: Added Gaussian noise to achieve specified Signal-to-Noise Ratios (SNR).
  • Multi-Trial Simulation: Refactored DataSimulator to support multi-trial simulations.
• Improved Noise Handling: Enhanced noise handling in both data simulation and source estimation processes.
• Refactoring:
  • Refactored the ERP signal generation within DataSimulator.
  • Refactored the data parameter grid for more flexible experiment configuration.

Affected Files:

• calibrain/data_simulation.py
• calibrain/benchmark.py
• examples/run_experiments.py

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Full Changelog: v0.1.0...v0.1.1

v0.1.0 - Initial Release

Initial public release of CaliBrain (v0.1.0)!

This version establishes the core foundation of CaliBrain, a Python package designed for simulating EEG/MEG data and benchmarking Brain Source Imaging (BSI) methods, with a focus on uncertainty estimation.

Core Components:

• LeadfieldSimulator: For simulating leadfield matrices (developed by @orabe).
• DataSimulator: For generating synthetic EEG/MEG data (developed by @orabe).
• SourceEstimator: For estimating source activity, with initial support for the Gamma-MAP method (developed by @orabe).
• UncertaintyEstimator: For estimating uncertainty in source activity (developed by @orabe).
• Benchmark: A class for systematically benchmarking source estimation methods (developed by @orabe).
• utils: A collection of utility functions (developed by @orabe).
• vbfa.py: Implementing Variational Bayes Factor Analysis for noise learning (#2 by @AliHashemi-ai).
• eLORETA_caliBrain.py: eLORETA implementation with posterior covariance matrix estimation (#3 by @IsmailHuseynov).

Contributors:

• @orabe
• @AliHashemi-ai
• @IsmailHuseynov

Full Changelog: https://github.com/braindatalab/CaliBrain/commits/v0.1.0