Abstract
We investigate a method to asses brain synchronization in individuals who fulfill a cooperation task. Our input is a couple of signals from functional Near-Infrared Spectroscopy Data Acquisition and Pre-processing technology that is used to capture the brain activity of an individual by measuring the oxyhemoglobin (HbO) level. Then, we use the visibility graph approach to map each HbO signal into a network. We estimate the signal synchronization by studying a global measure, related to eigenvalues of Laplacian matrix, in each constructed visibility graph. We consider the autonomous evolution of one isolated node to be a Rössler function. Then, the synchronization of signals can be characterized by a little number of parameters that could be employed to classify the sources of signal. Unlike prior research in this area, our aim is to examine the circumstances in which synchronization occurs in various individuals and within different hemispheres of the prefrontal cortexes of the same individual. Experimental results show that the conditions for synchronization vary in different individuals, and they are different even for the distinct prefrontal cortical hemispheres of the same individual.
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We would like to thank Guilhem Belda, President of the Semaxone company (Rochefort-du-Gard, France), for his help in developing the application and providing the audio equipment.
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Dhamo, X. et al. (2024). Global Synchronization Measure Applied to Brain Signals Data. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1144. Springer, Cham. https://doi.org/10.1007/978-3-031-53503-1_35
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