Class-in-Sync: Mobile EEG Hyperscanning in educational settings

Hyperscanning refers to the simultaneous recording of brain activity of multiple individuals with subsequent analysis of one brain activity in relation to other brain activities instead of external stimuli. Portable and affordable mobile electroencephalography (EEG) devices enable non-invasive and real-life recordings of brain wave dynamics (Babiloni & Astolfi, 2014). Mobile EEG devices have been shown to provide sufficient signal-acquisition quality for research in various neuroscience domains, including educational neuroscience (Czeszumski et al. 2020). Because the field of hyperscanning requires data processing metrics that are appropriate for a «brain-to-brain» rather than a «brain-to-stimuli» approach, new software libraries have emerged to meet this need (Ayrolles et al. 2021).

Mobile EEG hyperscanning is suitable for examining the brain during social interactions, e.g. in the classroom. The few studies that specifically address hyperscanning in educational neuroscience report that synchronization between students' brains is higher during interactive classroom activities than during passive classroom activities (Dikker et al., 2018). In parallel, data from educational science show that learning through social interaction improves student achievement (Furrer & Skinner, 2003). Efficient social interactions require a sense of inclusion, which is especially important in mixed groups of students with and without special needs. Another important pedagogical factor is the student-teacher relationship (Holper et al. 2013).

Data on hyperscanning in educational neuroscience is limited, and there are no dedicated hyperscanning studies tracking social brain patterns in groups of children in real-world educational settings. There is also no open discussion in the literature about the introduction of the hyperscanning method for evaluating teaching styles and educational environments and the regular collection of neural data in groups of students of different ages, including children with special needs. The novelty of the project is the application of mobile EEG hyperscanning with a variety of metrics of inter-brain synchronization in school children in real-world settings, with the goal of evaluating the feasibility of this method for further regular neural data collection for educational research and improvement of teaching practices in students of different ages, including children with special needs.

This study is a feasibility study and the first phase of a planned series of sequential studies designed to evaluate the feasibility of mobile EEG hyperscanning as a feedback method in educational settings. The project meets the criteria that are suitable for a feasibility study (Orsmond & Cohn, 2015).

EEG data and questionnaires were collected from six schoolchildren aged 10–11 during individual and interactive classroom activities. The first aim was to evaluate the quality and suitability of the collected EEG data for tracking social brain dynamics by applying hyperscanning analysis metrics. The second aim was to evaluate the correlation between students' responses to the questionnaires and social brain dynamics during individual and interactive class activities.

The results of our preliminary work will be incorporated into this study. We have developed a hyperscanning software pipeline and conducted a pilot study that demonstrated the efficiency of the developed software pipeline for hyperscanning analysis and the applicability of the selected metrics for assessing the level of synchronization of brain activities.

The feasibility assessment of mobile EEG hyperscanning in educational settings can demonstrate the advantages and limitations of this method and outline directions for regular neural data acquisition and EEG-based feedback in groups of students of different ages, including children with special needs.

The introduction of mobile EEG hyperscanning techniques into educational settings can also contribute to the growth of neural data on social development. On this basis, individual and group-specific variance can be estimated and used for feedback on teaching environments (e.g., through machine learning algorithms). Feedback on teaching environments for teachers and teachers in special education can in turn be used to optimize inclusive school environments (Wilcox et al. 2021).

• Ayrolles, A., Brun, F., Chen, P., Amir Djalovski, A., Beauxis, Y., Delorme R., Bourgeron, T., Dikker, S., Guillaume Dumas, G. (2021). HyPyP: a Hyperscanning Python Pipeline for inter-brain connectivity analysis. Social Cognitive and Affective Neuroscience, 16(1–2), 72-83. doi: 10.1093/scan/nsaa141
• Babiloni, F., Astolfi, L. (2014). Social neuroscience and hyperscanning techniques: Past, present and future. Neurosci Biobehav Rev, 44, 76–93. doi: 10.1016/j.neubiorev.2012.07.006 
• Czeszumski, A., Eustergerling, S., Lang, A., Menrath, D., Gerstenberger, M., Schuberth, S., Schreiber F., Rendon Z. Z., König P. (2020). Hyperscanning: A Valid Method to Study Neural Inter-brain Underpinnings of Social Interaction. Front Hum Neurosci, 14, 39. doi: 10.3389/fnhum.2020.00039
• Dikker, S., Wan, L., Davidesco, I., Kaggen, L., Oostrik, M., McClintock, J., Rowland, Michalareas, G., Van Bavel, J.J., Ding, M., Poeppel, D. (2018). Brain-to-Brain Synchrony Tracks Real-World Dynamic Group Interactions in the Classroom. Curr Biol, 27(9), 1375–1380. doi: 10.1016/j.cub.2017.04.002
• Furrer, C., & Skinner, E. (2003). Sense of relatedness as a factor in children’s academic engagement and performance. Journal of Educational Psychology, 95(1), 148–162. doi: 10.1037/0022-0663.95.1.148 
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• Wilcox, G., Morett, L.M., Hawes, Y., Dommett, E.J. (2021). Why Educational Neuroscience Needs Educational and School Psychology to Effectively Translate Neuroscience to Educational Practice. Front Psychol, 11, 618449. doi: 10.3389/fpsyg.2020.618449