EEGUnity

EEGUnity facilitates the integration and processing of diverse EEG datasets to address challenges in managing large-scale EEG model research.

Key Features:

• EEG Parser: Enables intelligent data structure inference for parsing multiple EEG datasets with variable content data, metadata, and data formats.
• Correction: Performs data cleaning to ensure high data quality and consistency across integrated datasets.
• Batch Processing: Supports efficient management and unification of multiple EEG datasets through automated workflows.
• Large Language Model Boost: Integrates large language model functionality to enhance dataset processing capabilities.

Scientific Applications:

• Large-scale EEG model research: Provides a reliable foundation for large-scale EEG data research by ensuring high data quality and consistency across diverse datasets.
• Multi-dataset integration: Enables efficient management of multiple EEG datasets from different sources, evaluated across 25 EEG datasets.

Methodology:

EEGUnity employs modular architecture with four components: EEG Parser for data structure inference, Correction for data cleaning, Batch Processing for workflow automation, and Large Language Model Boost for enhanced processing.