A Data-Driven Approach to Multimodal Analysis of Inter-Brain and Behavioral Synchrony

Abstract

This paper investigates the behavioral and inter-brain synchronization aspects of remote learning through the use of multimodal analysis. Students' cognitive and social alignment will be evaluated in this investigation as they utilize digital platforms for participation. Synchrony is evaluated through the use of neurophysiological signals, such as EEG-based inter-brain coherence, interaction patterns, facial expressions, and eye movements. We employ advanced signal processing and machine learning to integrate data sources. Subsequently, trends in collaboration, participation, and learning objectives are identified. Academic performance, group attention, and communication are enhanced even in the absence of individuals when inter-brain synchrony is high. This paper demonstrates that synchrony-aware analytics enhances academic performance, adaptive training, and virtual collaboration. This enhances the development of intelligent remote learning systems.