Implementation of a Real-Time Brain-to-Brain Synchrony Estimation Algorithm for Neuroeducation Applications

Area of Science:

• Neuroscience
• Educational Technology
• Computational Neuroscience

Background:

• Current educational methods lack objective feedback metrics for social interactions.
• Neuroeducation and hyperscanning research highlight the need for biomarkers of brain synchrony.
• Understanding brain-to-brain synchronization is crucial for optimizing collaborative and competitive learning environments.

Purpose of the Study:

• To develop a real-time algorithm for estimating brain-to-brain synchronization during social interactions.
• To apply this algorithm in educational contexts, such as teacher-student and student-student interactions.
• To provide a novel biomarker for feedback in teaching and learning processes.

Main Methods:

• Implementation of the bispectrum technique using multiprocessing in Python.
• Processing of electroencephalography (EEG) signals to estimate brain-to-brain synchronization.
• Validation of results through statistical testing on data from collaborative and competitive tasks.

Main Results:

• The algorithm reliably detected significant differences in brain-to-brain synchronization between collaborative and competitive tasks.
• Higher bispectrum values were observed during collaborative activities compared to competitive ones.
• 33.75% of the tested results showed statistical significance, validating the algorithm's efficacy.

Conclusions:

• The developed algorithm provides a reliable method for quantifying brain-to-brain synchronization in real-time.
• This tool has significant potential applications in neuroeducation, classrooms, and industries.
• The open-source nature and adaptability of the algorithm facilitate broader research and implementation.