Synchronization of chaotic electroencephalography (EEG) signals

Jessica Zaqueros-Martinez*, Gustavo Rodriguez-Gomez, Esteban Tlelo-Cuautle, Felipe Orihuela-Espina

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review


Synchronization of chaotic signals often considers a master-slave paradigm where a slave chaotic system is required to follow the master also chaotic. Most times in literature both systems are known, but synchronization to some unknown master has a potentially large range of applications, for example, EEG based authentication. We aim to test the feasibility of fuzzy control to systematically synchronize a chaotic EEG record. In this chapter, we study the suitability of two chaotic systems and the companion fuzzy control strategies under complete and projective synchronization to synchronize to EEG records. We used two public EEG datasets related to the genetic predisposition to alcoholism and with detecting emotions respectively. We present a comparative study among fuzzy control strategies for synchronization of chaotic systems to EEG records on selected datasets. As expected, we observed success and failures alike on the synchronization highlighting the difficulty in achieving this kind of synchronization, but we interpret this as advantageous for purposes of the suggested domain application. With successful synchronizations, we confirm that synchronization is feasible. With unsuccessful synchronizations, we illustrate that synchronization of chaotic systems does not follow a simple one-size-fits-all recipe and we attempt to gain insight for future research. The same chaotic system may succeed or fail depending on its companion type of synchronization and controller design.

Original languageEnglish
Title of host publicationCybersecurity
Subtitle of host publicationA New Approach Using Chaotic Systems
EditorsAhmed A. Abd El-Latif, Christos Volos
Number of pages26
ISBN (Electronic)9783030921668
ISBN (Print)9783030921651
Publication statusPublished - 26 Mar 2022

Publication series

NameStudies in Big Data
ISSN (Print)2197-6503
ISSN (Electronic)2197-6511

Bibliographical note

Funding Information:
Acknowledgements The author J. Zaqueros-Martinez was supported by the Mexican Research Council (CONACYT no. 776446).

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Engineering (miscellaneous)


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