EEGSourceSim

EEGSourceSim simulates realistic electroencephalography (EEG) scalp data using MRI-based forward models to assess and validate EEG analysis methodologies.

Key Features:

• MRI-Based Forward Models: Uses individual MRI-derived head models and forward solutions to generate anatomically informed EEG scalp signals.
• Biologically Plausible Signals and Noise: Embeds realistic signal and noise by fitting multiple noise components to measured resting-state EEG data.
• User-Specifiable Signal-to-Noise Ratio (SNR): Allows specification of SNR to model different experimental noise conditions.
• Comprehensive Pipelines: Provides pipelines to evaluate source estimation, functional connectivity, and spatial filtering methods.
• Quantitative Metrics: Supplies metrics tailored to assess performance in source estimation, connectivity analysis, and spatial filtering.
• Enhanced Head Models: Includes surface-based head models with two ROI sets (a whole-brain atlas and a visual cortex atlas) aligned to individuals via surface-based registration.

Scientific Applications:

• Validation of EEG Analysis Methods: Enables rigorous testing of source estimation, connectivity, and spatial filtering algorithms using realistic simulated data.
• Interpretability Studies: Supports investigations into how anatomical variability and noise characteristics affect EEG result interpretability.
• Algorithm Development: Facilitates development and refinement of algorithms for source estimation, connectivity analysis, and spatial filtering.

Methodology:

Integrates MRI-based forward models with biologically plausible signal and noise, fits noise components to measured resting-state EEG, and uses individual MRI-based surface forward solutions with surface-based registration to align atlases to each subject.