Offline EEG hyper-scanning using anonymous walk embeddings in tacit coordination games.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Graph Theory

Background:

• Hyper-scanning studies typically require simultaneous electroencephalography (EEG) recordings to examine inter-brain synchrony.
• Analyzing brain synchrony is crucial for understanding social cognition and coordination.
• Existing methods often rely on direct node-level data, limiting applicability.

Purpose of the Study:

• To develop a novel method for assessing brain synchrony without simultaneous EEG recordings.
• To investigate the effectiveness of anonymous random walks in capturing graph structures for brain data analysis.
• To predict successful tacit coordination between individuals based on their brain activity patterns.

Main Methods:

• Utilized anonymous random walks to create spatial encodings of graph structures from EEG data.
• Analyzed offline EEG data from players engaged in tacit coordination games.
• Employed a classification model using the spatial distance between player brain patterns (vector embeddings) to predict coordination success.

Main Results:

• Anonymous random walks successfully encoded graph structures irrespective of individual node labels.
• The classification model, using spatial distances between brain patterns, achieved approximately 85% accuracy in predicting coordination success.
• Demonstrated the feasibility of analyzing inter-brain synchrony without simultaneous recordings.

Conclusions:

• The proposed method offers a viable alternative for studying brain synchrony in coordination tasks.
• Anonymous random walks provide a robust approach for analyzing complex network patterns in neural data.
• This technique has significant implications for understanding social interaction and coordination in neuroscience.