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ADHD Children Identification Based on EEG Using Effective Connectivity Techniques

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 13079)

Abstract

This paper presents a novel method to identify the Attention deficit hyperactivity disorder (ADHD) children using electroencephalography (EEG) signals and effective connectivity techniques. In this study, the original EEG data is pre-filtered and divided into Delta, Theta, Alpha and Beta bands. And then, the effective connectivity graphs are constructed by applying independent component analysis, multivariate regression model and phase slope index. The measures of clustering coefficient, nodal efficiency and degree centrality in graph theory are used to extract features from these graphs. Statistical analysis based on the standard error of the mean is employed to evaluate the performance in each frequency band. The results show a decreased average clustering coefficient in Delta band for ADHD subjects. Also, in Delta band, the ADHD subjects have increased nodal efficiency and degree centrality in left forehead and decreased in forehead middle.

Keywords

  • ADHD
  • EEG
  • Effective connectivity
  • Multivariate regression model
  • Phase slope index
  • Graph theory

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Acknowledgment

We acknowledge the material support by Ali Motie Nasrabadi, Armin Allahverdy, Mehdi Samavati, Mohammad Reza Mohammadi shared on the IEEE Data port (https://doi.org/10.21227/rzfh-zn36).

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Correspondence to Mingkan Shen .

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Shen, M., Wen, P., Song, B., Li, Y. (2021). ADHD Children Identification Based on EEG Using Effective Connectivity Techniques. In: Siuly, S., Wang, H., Chen, L., Guo, Y., Xing, C. (eds) Health Information Science. HIS 2021. Lecture Notes in Computer Science(), vol 13079. Springer, Cham. https://doi.org/10.1007/978-3-030-90885-0_7

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  • DOI: https://doi.org/10.1007/978-3-030-90885-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90884-3

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