Reduced functional integration and connectivity in EEG-based functional brain networks during boredom: A...

Boredom is a cognitive-affective state characterized by low attention and arousal. Its importance has been widely recognized across multiple domains, including education, where it negatively affects performance and academic achievement. However, its underlying neural mechanisms remain poorly understood. In this study, changes in brain network structure during non-bored and bored conditions were analyzed using EEG and graph-theoretical measures. A sample of twenty-five university students watched an educational video intended to induce boredom while their brain activity was recorded with an EEG. Following preprocessing, functional brain networks (FBNs) were constructed for each participant using a pairwise nonlinear mutual information (MI) measure applied to segmented EEG data. The resulting undirected, weighted, fully connected networks were sparsified using the Minimum Connected Component (MCC) thresholding algorithm. The resulting thresholded FBNs were then analyzed using global network metrics, which were computed and compared at the individual level between the non-boredom and boredom states. The experimental results showed that changes in brain network topology persisted throughout the participant's boredom state, with lower edge density, average degree centrality, clustering coefficient, and global efficiency, together with higher characteristic path length. Consequently, boredom disrupts both clustering within small-scale regions and integration across large-scale brain areas due to attentional disengagement and inefficient transfer of information. The study findings contribute to affective neuroscience by identifying neural markers of boredom and suggesting the possibility of developing a personalized EEG-based boredom-aware system for educational contexts and other fields.