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.

PMID: 35367895
PMCID: PMC8980555
Funding: - Deutsche Forschungsgemeinschaft: 402789467