Imperial College London
Dr Felipe Orihuela-Espina

DrFelipeOrihuela-Espina

Faculty of Medicine, Department of Surgery & Cancer

Honorary Lecturer
 
 
 
//

Contact

 

f.orihuela-espina

 
 
//

Location

 

//

Summary

 

Publications

Citation

BibTex format

@inbook{Zaqueros-Martinez:2022:10.1007/978-3-030-92166-8_5,
author = {Zaqueros-Martinez, J and Rodriguez-Gomez, G and Tlelo-Cuautle, E and Orihuela-Espina, F},
booktitle = {Studies in Big Data},
doi = {10.1007/978-3-030-92166-8_5},
pages = {83--108},
title = {Synchronization of Chaotic Electroencephalography (EEG) Signals},
url = {http://dx.doi.org/10.1007/978-3-030-92166-8_5},
year = {2022}
}
Download

RIS format (EndNote, RefMan)

TY  - CHAP
AB  - 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.
AU  - Zaqueros-Martinez,J
AU  - Rodriguez-Gomez,G
AU  - Tlelo-Cuautle,E
AU  - Orihuela-Espina,F
DO  - 10.1007/978-3-030-92166-8_5
EP  - 108
PY  - 2022///
SP  - 83
TI  - Synchronization of Chaotic Electroencephalography (EEG) Signals
T1  - Studies in Big Data
UR  - http://dx.doi.org/10.1007/978-3-030-92166-8_5
ER  -
Download