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Connectivity Analysis

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EEG Signal Processing and Feature Extraction

Abstract

The EEG signals could be used to assess the communication between brain regions. Various techniques have been developed in order to quantify the EEG connectivity of scalp-level EEG signals or source-level activities. Briefly speaking, four kinds of EEG connectivity measures are evaluated in literatures, including coherence-based measures, phase synchronization-based measures, generalized synchronization-based measures, and granger causality-based measures. All measures have their own advantages and disadvantages. Here, we illustrated the common sources problem in EEG analysis, the measures in EEG connectivity analysis, how to conduct EEG connectivity analysis using resting-state EEG signals and event-related EEG signals, and source-level connectivity. Moreover, we provided two examples of EEG connectivity, along with the EEG datasets and MATLAB codes, which are focused on the EEG connectivity of resting-state signals and event-related signals, respectively.

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Author information

Authors and Affiliations

  1. Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, Jiangsu, China

    Huibin Jia

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  1. Huibin Jia
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Editor information

Editors and Affiliations

  1. CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China

    Li Hu

  2. School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong, China

    Zhiguo Zhang

12.1 Supplementary Electronic Material (S)

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© 2019 Springer Nature Singapore Pte Ltd.

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Jia, H. (2019). Connectivity Analysis. In: Hu, L., Zhang, Z. (eds) EEG Signal Processing and Feature Extraction. Springer, Singapore. https://doi.org/10.1007/978-981-13-9113-2_12

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