HAPPE+ER

HAPPE+ER automates standardized preprocessing of EEG-derived event-related potential (ERP) data to produce quality-controlled ERP outputs for neurocognitive research.

Key Features:

• Automated Processing Pipeline: Processes raw EEG to processed ERP outputs with steps including filtering, line noise reduction, bad channel detection, artifact rejection, segmentation, and bad segment rejection.
• Standardization: Provides a consistent preprocessing workflow that reduces variability from manual editing and supports reproducible ERP analyses.
• Quality Metrics Reporting: Generates comprehensive post-processing reports detailing data quality and pipeline performance metrics.
• Optimization for Diverse Populations: Includes methodologies validated and tailored for adult and developmental datasets, including clinical populations.
• Post-processing Scripts: Supplies scripts to generate ERP figures and measures necessary for statistical analysis.

Scientific Applications:

• Cognitive neuroscience: Supports ERP studies investigating cognitive processes using EEG-derived ERPs.
• Developmental neuroscience: Enables preprocessing of developmental EEG/ERP datasets for longitudinal and cross-sectional studies.
• Clinical research and diagnostics: Facilitates ERP analyses in clinical populations for research and diagnostic investigations.
• High-throughput and large datasets: Allows standardized preprocessing for studies with large sample sizes or high-throughput data collection.

Methodology:

Automated computational steps explicitly include filtering and line-noise reduction, bad channel detection and exclusion, artifact rejection, segmentation into event-centered epochs, bad segment rejection, generation of quality metrics, and post-processing scripts to produce ERP figures and measures.