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EEG emotion recognition based on PLV-rich-club dynamic brain function network

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Abstract

During emotional changes, the brain generates many highly connected and highly concentrated hub regions. Thus, barely studying the whole-brain network architecture while ignoring the connection information between small-scale structures, will not be able to accurately differentiate the information interaction patterns of the brain under different emotions. The connections between high-degree nodes in the brain are stronger than those between low-degree nodes. The rich-club structure composed by those high-degree nodes is a hot topic in the current research of emotion recognition. Therefore, this paper investigates the dynamics of the rich-club structure generated by high-degree nodes in different time periods during different emotional changes. Firstly, the dynamic PLV brain network is constructed using non-overlapping time windows. Afterwards, the rich-club coefficients in the brain network are calculated and the nodes with higher degrees are selected as rich-club nodes within the coefficient range. Furthermore, the ReliefF algorithm is used to filter the frequency domain features of rich-club nodes and PLV-rich-club graph-theoretical features to derive the most emotionally relevant features for emotion recognition. The experimental results show that the composition of the rich-club is roughly the same but there are subtle differences over time. The smaller the valence, the larger the K-degree range, in which the structure of the rich-club is more stable, and the information interaction is more complex. The larger the valence, in which the information contained by the rich-club structure is more modular, and its ability to transmit information is stronger. It is revealed that the key to distinguishing different emotions is the brain information represented by the connections between small-scale structures in complex brain networks. The LOSO validation method was used for verification on the DEAP and SEED datasets, and the features based on the rich-club structure achieved 86.11% and 87.92% accuracy in the valence dimension, respectively.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62002287,in part by the National Natural Science Foundation of China under Grant 61373116,in the part by the Shanxi Provincial Key Research and Development Project under Grant 2022SF-037,in the part by the Xi’an University of Posts and Telecommunications under Grant CXJJZL2021017.

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Authors and Affiliations

  1. School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Zhong-Min Wang, Zhe-Yu Chen & Jie Zhang

  2. Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Zhong-Min Wang & Jie Zhang

  3. Xi’an Key Laboratory of Big Data and Intelligent Computing, Xi’an, 710121, Shaanxi, China

    Zhong-Min Wang & Jie Zhang

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  1. Zhong-Min Wang
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Appendix: A

Appendix: A

$$ \begin{array}{@{}rcl@{}} &&DE(x) \\ &=& - {\int}_{- \infty }^{+ \infty } \frac{1}{{\sqrt {2\pi {\sigma^{2}}} }}{e^{- \frac{{{{(x - \mu )}^{2}}}}{{2{\sigma^{2}}}}}}\log \left( {\frac{1}{{\sqrt {2\pi {\sigma^{2}}} }}{e^{- \frac{{{{(x - \mu )}^{2}}}}{{2{\sigma^{2}}}}}}} \right)dx\\ &=& - {\int}_{- \infty }^{+ \infty } \frac{1}{{\sqrt {2\pi {\sigma^{2}}} }}{e^{- \frac{{{{(x - \mu )}^{2}}}}{{2{\sigma^{2}}}}}}\left( {\log \frac{1}{{\sqrt {2\pi {\sigma^{2}}} }} - \frac{{{{(x - \mu )}^{2}}}}{{2{\sigma^{2}}}}} \right)dx\\ &=& - {\int}_{- \infty }^{+ \infty } \left( \log \frac{1}{{\sqrt {2\pi {\sigma^{2}}} }} \cdot \frac{1}{{\sqrt {2\pi {\sigma^{2}}} }}{e^{- \frac{{{{(x - \mu )}^{2}}}}{{2{\sigma^{2}}}}}}\right)dx \\ &&- \left( \frac{{{{(x - \mu )}^{2}}}}{{2{\sigma^{2}}}} \cdot \frac{1}{{\sqrt {2\pi {\sigma^{2}}} }}{e^{- \frac{{{{(x - \mu )}^{2}}}}{{2{\sigma^{2}}}}}}\right)dx\\ &=& - {\int}_{- \infty }^{+ \infty } \log \frac{1}{{\sqrt {2\pi {\sigma^{2}}} }} \cdot \frac{1}{{\sqrt {2\pi {\sigma^{2}}} }}{e^{- \frac{{{{(x - \mu )}^{2}}}}{{2{\sigma^{2}}}}}}dx \\ &&+ {\int}_{- \infty }^{+ \infty } \frac{{{{(x - \mu )}^{2}}}}{{2{\sigma^{2}}}} \cdot \frac{1}{{\sqrt {2\pi {\sigma^{2}}} }}{e^{- \frac{{{{(x - \mu )}^{2}}}}{{2{\sigma^{2}}}}}}dx\\ &=& -\log \frac{1}{\sqrt{2\pi \sigma^{2}}}{\int}_{- \infty }^{+ \infty } \frac{1}{{\sqrt {2\pi {\sigma^{2}}} }}{e^{- \frac{{{{(x - \mu )}^{2}}}}{{2{\sigma^{2}}}}}}dx \\ && + \frac{1}{{2{\sigma^{2}}}}{\int}_{- \infty }^{+ \infty } {{(x - \mu )}^{2}}\frac{1}{{\sqrt {2\pi {\sigma^{2}}} }}{e^{- \frac{{{{(x - \mu )}^{2}}}}{{2{\sigma^{2}}}}}}dx\\ &=& \frac{1}{2}\log 2\pi {\sigma^{2}} + \frac{1}{{2{\sigma^{2}}}}\times {\sigma^{2}}\\ &=& \frac{1}{2}\log 2\pi {\sigma^{2}} + \frac{1}{2}\\ &=& \frac{1}{2}\log 2\pi {\sigma^{2}} + \frac{1}{2} \log e\\ &=& \frac{1}{2}\log 2\pi e{\sigma^{2}} \end{array} $$

Where \({\int \limits }_{- \infty }^{+ \infty } \frac {1}{{\sqrt {2\pi {\sigma ^{2}}} }}{e^{- \frac {{{{(x - \mu )}^{2}}}}{{2{\sigma ^{2}}}}}}dx\) obeys the Gaussian distribution, \({\int \limits }_{- \infty }^{+ \infty } \frac {1}{{\sqrt {2\pi {\sigma ^{2}}} }}{e^{- \frac {{{{(x - \mu )}^{2}}}}{{2{\sigma ^{2}}}}}}dx = 1 \) \({\int \limits }_{- \infty }^{+ \infty } {{(x - \mu )}^{2}}\frac {1}{{\sqrt {2\pi {\sigma ^{2}}} }}{e^{- \frac {{{{(x - \mu )}^{2}}}}{{2{\sigma ^{2}}}}}}dx =\\ {\int \limits }_{- \infty }^{+ \infty } {{x}^{2}}\frac {1}{{\sqrt {2\pi {\sigma ^{2}}} }}{e^{- \frac {{{{x}^{2}}}}{{2{\sigma ^{2}}}}}}dx=\sigma ^{2}\)

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Wang, ZM., Chen, ZY. & Zhang, J. EEG emotion recognition based on PLV-rich-club dynamic brain function network. Appl Intell 53, 17327–17345 (2023). https://doi.org/10.1007/s10489-022-04366-7

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