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Multivariate EEG Synchronization Strength Measures

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Signal Processing in Neuroscience

Abstract

EEG synchronization is considered to be the important performance of the brain to inform, communicate, interact, and coordinate between various regions. There exist lots of bivariate methods to quantify the EEG synchronization between two EEG signals. However, multivariate data contain more information than those inferable from multiple bivariate examinations. Multivariate synchronization analysis aiming at the global information has been paid much more attention recently. It is a useful technique for studying the interactions in a group of multivariate channels for the understanding of overall dynamical properties in the brain. In this chapter, the multivariate synchronization methods including phase synchronization cluster analysis, S-estimator, correlation matrix analysis, omega complexity, partial directed coherence, directed transfer function, and complex network analysis and their applications in studying the relationship among neural signals are presented.

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Authors and Affiliations

  1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, People’s Republic of China

    Dong Cui

  2. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, People’s Republic of China

    Xiaoli Li

  3. Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, 100875, People’s Republic of China

    Xiaoli Li

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  1. Beijing Normal University, State Key Lab Cognit Neurosci & Learning, Beijing, Beijing, China

    Xiaoli Li

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Cui, D., Li, X. (2016). Multivariate EEG Synchronization Strength Measures. In: Li, X. (eds) Signal Processing in Neuroscience. Springer, Singapore. https://doi.org/10.1007/978-981-10-1822-0_12

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