APICE

APICE performs automated preprocessing of infant continuous electroencephalography (EEG) data by detecting and correcting artifacts to enable robust developmental EEG analyses.

Key Features:

• Automation: Fully automated preprocessing of infant EEG datasets without manual intervention.
• Flexibility and modularity: Modular pipeline adaptable across diverse age groups and experimental paradigms without parameter redefinition.
• Adaptive artifact detection: Employs multiple algorithms using adaptive thresholds for dataset-specific artifact detection.
• Continuous-data artifact correction: Detects and corrects artifacts on continuous EEG to preserve signal integrity without epoching.
• Transient artifact recovery: Addresses transient artifacts to enhance data recovery while maintaining continuous recordings.
• Source-separation integration: Integrates with Independent Component Analysis (ICA) and Denoising Source Separation (DSS) for denoising and component-based cleaning.
• EEGLAB compatibility: Implemented with EEGLAB-compatible custom functions.

Scientific Applications:

• Developmental EEG research: Preprocessing infant EEG across developmental stages to account for age-varying neural signals and artifacts.
• Continuous EEG analyses: Preservation of continuous signals for time-series, connectivity, and other continuous-data analyses.
• Cross-cohort and paradigm comparisons: Standardized preprocessing applicable to multiple age groups and experimental paradigms.
• Source separation workflows: Preprocessing upstream of ICA and DSS-based denoising and source analysis.

Methodology:

Applies multiple artifact-detection algorithms with adaptive thresholds, performs artifact detection and correction on continuous EEG without epoching, integrates with Independent Component Analysis (ICA) and Denoising Source Separation (DSS), and is implemented as EEGLAB-compatible custom functions.