Skip to main content
Springer Nature Link
Log in

Analysis of functional brain network based on electroencephalography and complex network

  • Technical Paper
  • Published:
Microsystem Technologies Aims and scope Submit manuscript

Abstract

In this paper, electroencephalography data are used to establish a functional network connecting correlated human brain regions. Through analysis, it is found that the resulting network shows statistical characteristics of a complex network: its clustering coefficient is orders of magnitude larger than that of the equivalent random network, which is typical of a small-world network, and the distribution of degree is close to that of a scale-free network. All these characteristics reflect important functional information about brain states. For alcohol addicts, the characteristic indices of their brains are obviously different from those of the control group. The information entropy and standard information entropy of the brain neural network are also defined to measure the characteristics of the complex network. This gives a new criterion for clinical diagnosis and treatment of encephalopathy. Calculation results indicate that the brain neural network information entropy of alcohol addicts is quite distinct from that of the control group.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Balenzuela P, Rué P, Boccaletti S (2014) Collective stochastic coherence and synchronizability in weighted scale-free networks. New J Phys 16:331–344

    Article  Google Scholar 

  • Diykh M, Li Y (2016) Complex networks approach for EEG signal sleep stages classification. Expert Syst Appl 63:241–248

    Article  Google Scholar 

  • Dodel S, Herrmann JM (2002) Functional connectivity by cross-correlation clustering. Neurocomputing 44:1065–1070

    Article  Google Scholar 

  • Eguiluz VM, Cecchi G, Chialvo DR (2003) Scale-free structure of brain functional networks. Phys Rev Lett 94:273–278

    Google Scholar 

  • Friston KJ (2005) Models of brain function in neuroimaging. Annu Rev Psychol 56:57–87

    Article  Google Scholar 

  • Friston K, Phillips J, Chawla D (1999) Revealing interactions among brain systems with nonlinear PCA. Hum Brain Mapp 8:92–105

    Article  Google Scholar 

  • Horwitz B (2003) The elusive concept of brain connectivity. Neuroimage 19:466–470

    Article  Google Scholar 

  • Ingber L (2012) Columnar electromagnetic influences on short-term memory at multiple scales. Soc Sci Electr Publ 343:138–153

    Google Scholar 

  • Ingber L (2016) Statistical mechanics of neocortical interactions: large-scale EEG influences on molecular processes. J Theor Biol 395:144–152

    Article  MathSciNet  Google Scholar 

  • Ingber L, Nunez PL (1990) Multiple scales of statistical physics of the neocortex: application to electroencephalography. Math Comput Model 13:83–95

    Article  Google Scholar 

  • Mcintosh AR, Bookstein FL, Haxby JV (1996) Spatial pattern analysis of functional brain images using partial least squares. Neuroimage 3:143–157

    Article  Google Scholar 

  • Penny WD, Stephan KE, Mechelli A (2004) Modelling functional integration: a comparison of structural equation and dynamic causal models. Neuroimage 23:264–274

    Article  Google Scholar 

  • Power J, Cohen A, Nelson S (2011) Functional network organization of the human brain. Neuron 72:665–678

    Article  Google Scholar 

  • Shamshiri EA, Tierney TM, Centeno M (2017) Interictal activity is an important contributor to abnormal intrinsic network connectivity in paediatric focal epilepsy. Hum Brain Mapp 38:221–236

    Article  Google Scholar 

  • Sun G, Bin S (2018) A new opinion leaders detecting algorithm in multi-relationship online social networks. Multimed Tools Appl 77:4295–4307

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the Humanity and Social Science Youth foundation of Ministry of Education of China under Grant no. 15YJC860001. This research is also supported by Shandong Provincial Natural Science Foundation, China under Grant no. ZR2017MG011 and China Postdoctoral Science Foundation Funded Project under Grant nos. 2016T90606 and 2018T110663.

Author information

Authors and Affiliations

  1. School of Data Science and Software Engineering, Qingdao University, Qingdao, 266071, China

    Sheng Bin & Gengxin Sun

  2. Information and Engineering College, Jimei University, Fujian, 361021, China

    Chih-Cheng Chen

Authors
  1. Sheng Bin
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Gengxin Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Chih-Cheng Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Chih-Cheng Chen.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bin, S., Sun, G. & Chen, CC. Analysis of functional brain network based on electroencephalography and complex network. Microsyst Technol 27, 1525–1533 (2021). https://doi.org/10.1007/s00542-019-04424-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00542-019-04424-0