DEEP: A dual EEG pipeline for developmental hyperscanning studies.

Area of Science:

• Social Neuroscience
• Neuroimaging
• Computational Neuroscience

Background:

• Hyperscanning enables measuring neural synchrony between individuals in naturalistic settings.
• Advancements in hyperscanning necessitate robust and transparent data analysis techniques.
• Existing methods may lack comprehensive validation and accessibility for social neuroscience research.

Purpose of the Study:

• To introduce and validate DEEP, a novel dual electroencephalography (EEG) analysis pipeline.
• To provide tools for quantifying inter-brain phase alignment and neural activity.
• To promote open and reproducible science in hyperscanning research.

Main Methods:

• Development and testing of the DEEP analysis pipeline for dual EEG data.
• Preprocessing of EEG data followed by calculation of Phase Locking Values (PLVs).
• Inclusion of scripts for controlling spurious correlations and analyzing time-frequency responses.

Main Results:

• The DEEP pipeline successfully calculates PLVs and cross-frequency PLVs for dyads.
• The pipeline analyzes time-frequency responses and EEG power for individual participants.
• Included scripts effectively control for spurious correlations in hyperscanning data.

Conclusions:

• DEEP offers a validated and transparent analysis framework for dual EEG hyperscanning.
• The pipeline facilitates the study of inter-brain synchrony in social neuroscience.
• Public release of DEEP and a sample dataset supports reproducible research practices.