An Analytical Approach for Naturalistic Cooperative and Competitive EEG-Hyperscanning Data: A Proof-of-Concept Study

Area of Science:

• Neuroscience
• Social Neuroscience
• Cognitive Neuroscience

Background:

• Understanding neural mechanisms of joint actions is crucial for social contexts.
• Existing analysis methods for hyperscanning data in naturalistic settings are limited.
• Cooperative and competitive interactions involve complex brain network dynamics.

Purpose of the Study:

• To propose and validate a novel analytical pipeline for hyperscanning electroencephalography (EEG) data.
• To analyze neural mechanisms of cooperative and competitive joint actions.
• To enable effective analysis of brain connectivity during naturalistic, turn-based motor tasks.

Main Methods:

• Developed an analytical pipeline for hyperscanning EEG data.
• Utilized corrected imaginary part of the phase locking value (ciPLV) for functional connectivity.
• Calculated hyperbrain, within-brain, and between-brain connectivity in theta, alpha, and beta bands.
• Applied graph theoretical measures and clustering for network characterization.

Main Results:

• The pipeline was validated on EEG data from dyads playing cooperative and competitive table tennis.
• Functional connectivity maps revealed distinct network features for cooperation and competition.
• The proposed methods successfully characterized brain networks during joint actions.

Conclusions:

• The developed pipeline is a promising tool for analyzing joint action EEG data.
• This approach facilitates the investigation of neural underpinnings of cooperation and competition.
• The findings support the utility of the pipeline for future social neuroscience research.