DEEP
DEEP implements a dual electroencephalography (EEG) hyperscanning pipeline for quantifying inter-brain neural synchronization in dyads during naturalistic social interactions.
Key Features:
- Dual EEG hyperscanning pipeline: Processes simultaneous dual EEG recordings from interacting dyads to enable inter-brain analyses.
- Phase Locking Values (PLVs): Calculates Phase Locking Values (PLVs) as indices of inter-brain phase alignment between dyads.
- Cross-Frequency Phase Locking Values: Computes cross-frequency PLVs to assess phase relationships across different frequency bands between participants.
- Time-frequency responses and EEG power analysis: Performs time-frequency analyses and EEG power estimation to characterize temporal and spectral dynamics within and across participants.
- Control for spurious correlations: Includes scripts to mitigate spurious correlations to distinguish genuine inter-brain synchrony from artifacts.
Scientific Applications:
- Developmental hyperscanning studies: Analyzes neural dynamics in developmental contexts to study social interaction-related synchronization.
- Mother-Infant Interactions: Applied to mother-infant EEG hyperscanning datasets to investigate early caregiver–child neural synchrony.
- Social neuroscience research: Enables investigation of neural mechanisms underlying communication, empathy, and other social behaviors via inter-brain synchrony metrics.
Methodology:
Preprocessing of EEG data, calculation of PLVs and cross-frequency PLVs, time-frequency analyses and EEG power estimation, and application of scripts to control for spurious correlations.
Topics
Details
- License:
- GPL-3.0
- Cost:
- Free of charge (with restrictions)
- Tool Type:
- workflow
- Programming Languages:
- MATLAB
- Added:
- 7/20/2022
- Last Updated:
- 11/24/2024
Operations
Publications
Kayhan E, Matthes D, Marriott Haresign I, Bánki A, Michel C, Langeloh M, Wass S, Hoehl S. DEEP: A dual EEG pipeline for developmental hyperscanning studies. Developmental Cognitive Neuroscience. 2022;54:101104. doi:10.1016/j.dcn.2022.101104. PMID:35367895. PMCID:PMC8980555.