SEREEGA: Simulating event-related EEG activity.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Biomedical Engineering

Background:

• Evaluating electroencephalography (EEG) analysis methods is challenging due to the lack of ground-truth brain activity data.
• Simulated EEG data is crucial for testing and validating signal processing, machine learning algorithms, and source reconstruction techniques.
• Existing simulation tools are often limited in scope or not publicly available.

Purpose of the Study:

• To introduce SEREEGA, a novel, free, and open-source MATLAB-based toolbox for simulating electroencephalography (EEG) data.
• To provide a unified platform that integrates various EEG simulation methods previously reported in the literature.
• To facilitate the generation of realistic, event-related EEG data with ground-truth for method evaluation.

Main Methods:

• SEREEGA is a modular and extensible toolbox designed for generating simulated EEG epochs.
• It supports five publicly available head models and simulates diverse signal types mimicking brain activity.
• The toolbox's architecture and workflow are presented, along with a demonstration dataset.

Main Results:

• SEREEGA enables the application of established analysis pipelines and classification methods to simulated data.
• The generated simulated EEG data produces realistic results, suitable for method validation.
• The toolbox successfully unifies multiple past simulation approaches into a single, accessible platform.

Conclusions:

• SEREEGA serves as a general-purpose toolbox for simulating ground-truth EEG data.
• It addresses the need for a comprehensive and accessible tool for EEG data simulation.
• The toolbox empowers researchers to rigorously evaluate and develop novel EEG analysis techniques.