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Reduced functional integration and connectivity in EEG-based functional brain networks during boredom: A
Rajamanickam Yuvaraj1, Thilaga Manickam2, Arjun Pulliyasseri2
1Science of Learning in Education Centre (SoLEC), Office for Research (OfR), National Institute of Education (NIE), Nanyang Technological University (NTU), 1 Nanyang Walk, 637616, Singapore.
Neuroscience
|May 30, 2026
Summary
Boredom, characterized by low attention, alters brain network structure. This study reveals how functional brain networks change during boredom, impacting information transfer and academic performance.
Area of Science:
- Affective Neuroscience
- Cognitive Neuroscience
- Educational Psychology
Background:
- Boredom is a common state affecting attention and arousal, negatively impacting academic achievement.
- The neural underpinnings of boredom are not well understood.
- Understanding boredom's neural basis is crucial for educational and other applications.
Purpose of the Study:
- To investigate the changes in functional brain network (FBN) structure during boredom.
- To identify neural markers associated with the state of boredom.
- To explore potential applications of EEG-based boredom detection.
Main Methods:
- Electroencephalography (EEG) was used to record brain activity in 25 university students.
- Functional brain networks were constructed using nonlinear mutual information (MI).
- Graph-theoretical measures were applied to analyze network topology changes between boredom and non-boredom states.
Main Results:
- Boredom was associated with decreased edge density, average degree centrality, clustering coefficient, and global efficiency.
- Characteristic path length increased during boredom, indicating less efficient information transfer.
- Altered brain network topology persisted during the boredom state.
Conclusions:
- Boredom disrupts brain network organization, impairing both local clustering and global integration of information.
- Findings identify neural signatures of boredom, suggesting potential for EEG-based monitoring systems.
- This research contributes to understanding the neural mechanisms of attention and affective states in learning environments.
