Abstract
Functional connectivity (FC) patterns in human brain are dynamic in a task-specific condition, and identifying the dynamic changes is important to reveal the information processing processes and network reconfiguration in cognitive tasks. In this study, we proposed a comprehensive framework based on high-order singular value decomposition (HOSVD) to detect the stable change points of FC using electroencephalogram (EEG). First, phase lag index (PLI) method was applied to calculate FC for each time point, constructing a 3-way tensor, i.e., connectivity \( \times \) connectivity \( \times \) time. Then a stepwise HOSVD (SHOSVD) algorithm was proposed to detect the change points of FC, and the stability of change points were analyzed considering the different dissimilarity between different FC patterns. The transmission of seven FC patterns were identified in a task condition. We applied our methods to EEG data, and the results verified by prior knowledge demonstrated that our proposed algorithm can reliably capture the dynamic changes of FC.
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Acknowledgments
This work was supported by National Natural Science Foundation of China (Grant No. 91748105), National Foundation in China (No. JCKY2019110B009), the Fundamental Research Funds for the Central Universities [DUT2019] in Dalian University of Technology in China, and the scholarships from China scholarship Council (No. 201706060263 & No. 201706060262). The authors would like to thank Dr. Peng Li for the provide of EEG data and Guanghui Zhang for the preprocessing work.
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Liu, W., Wang, X., Ristaniemi, T., Cong, F. (2020). Identifying Task-Based Dynamic Functional Connectivity Using Tensor Decomposition. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-63823-8_42
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DOI: https://doi.org/10.1007/978-3-030-63823-8_42
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