Abstract
In this work, we construct functional networks of the human brain using the coherence measure on the EEG time-series data, in response to external audio-visual stimuli. These stimuli were nine different movie clips selected to evoke different emotional states. The constructed networks for each emotion were characterized using network measures such as clustering coefficient, small worldness, the efficiency of information propagation, etc. in different frequency bands corresponding to brain waves. We used a community detection algorithm to infer the segregation of functional correlations in the brain into modules. Further, using the variation of information measure, we compare and contrast the modular organizations of different brain networks. We observe that the different brain networks are closest in their organization into modules in alpha frequency band while they farther apart in other bands. We identified crucial network nodes or hubs using centrality measure, and find that most of the hubs were common for all networks and belong to a specific location on the brain map. In summary, our work demonstrates the utilization of the network theoretical and statistical tools for understanding and differentiating different brain networks corresponding to the perception of varieties of emotional stimuli.
S. Jolad—Part of the work carried at: Indian Institute of Technology Gandhinagar, Gandhinagar-382355, India.
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Tripathi, R., Mukhopadhyay, D., Singh, C.K., Miyapuram, K.P., Jolad, S. (2020). Characterization of Functional Brain Networks and Emotional Centers Using the Complex Networks Techniques. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 882. Springer, Cham. https://doi.org/10.1007/978-3-030-36683-4_68
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